Research Interests

Theories and applications of neural networks, self-organising networks and deep learning
Image/video processing, enhancement, classification and face recognition
Nonstationary signal processing, financial time series analysis and prediction
Pattern recognition, data dimensionality reduction and manifold learning
Independent component analysis and blind deconvolution
Multidimensional data analytics and visualisation
Neuroinformatics and bioinformatics

Teaching, Admin and Leadership Responsibilities

MSc & 4th year MEng module on Machine Learning and Optimisation (since 2019)
MSc & 4th year MEng module on Digital Image Processing (since 2010)
MSc module on Digital Image Engineering (2010-2019)
4th year module on Advanced Signal Processing (2006-2009)
4th year module on Image Engineering (1999-2009)
3rd year module on Digital Signal Processing (2002-2005)
2nd year module on High Level Programming (1998-2002)
1st year module on Measurements and Analytical Software (2010-2016)

DSP MSc Course Director (2017-2019)
MEng Team Project Tutor (2011-2017)
BEng Electronic Engineering Course Tutor (2000-2014)

FSE Head of Business Engagement in AI and Big Data (since 2019)

Recent Activities

Chair of IDEAL 2019, held in Manchester on 14-16 November 2019. IDEAL is an annual international conference dedicated to emerging and challenging topics in intelligent data analytics and associated machine learning systems and paradigms. 2019 marked its 20th edition and after 17 years it returned to Manchester, the birthplace of Artificial Intelligence, with the support and co-sponsorship of the Alan Turing Institute, Springer and IEEE CIS UK & Ireland.

Winner of the IJCNN 2015 Time Series Prediction Competition with the work "Multistep Forecast Using an Extended Self-Organizing Regressive Neural Network" by Ouyang Yicun and Hujun Yin.

A recent plenary talk on Data Representation by Deep Learning at SOCO 2018, San Sebastian.

Keynote/Plenary Speaker at SOCO 2017, HAIS 2013, IEEE IST 2012, and CBIC 2011. Tutorial Speaker at VIIP 2009 and at WCCI 2008 (Title: Nonlinear Dimensionality Reduction and Data Visualisation). Plenary Speaker of HAIS 2008. Chair of Special Session on Principal Manifolds and Data Visualisation at IJCNN 2007. An organiser of Biologically Inspired Information Fusion Workshop, Surrey, 22-23 August 2006. A talk on "The Self-Organising Maps for Data Visualisation and Manifold Mapping" to Workshop on Principal Manifolds, Leicester, 24-26 August 2006. A partner of the UK ICA Research Network. Lectured on "Neural Networks" and "Self-Organising Maps" to The British Computer Society, Summer School 2003. Invited seminars to a number of universities, both within the UK and abroad, DTI Outreach Programme, EPSRC workshops on Data Mining and Visualisation, Networks on Systems Biology, as well as Neural Computing Application Forum. Here is an early review paper on nonlinear multidimensional data projection and visualisation. Here is a recent talk at IJCNN 2007 on SOMs and MDS.

Member of the EPSRC Peer Review College (since 2006).
Senior Member of the IEEE (since 2003).

Vice Chair of the IEEE CIS UK-Ireland Chapter (since Sept 2019).
Associate Editor, IEEE Transactions on Cybernetics (since 2015).
Associate Editor, IEEE Transactions on Neural Networks (2006-2010).
Editorial Board, International Journal of Neural Systems (since 2005).
Guest-Editor, Journal of Mathematical Modelling and Algorithms, Vol. 5, no. 4, 2006 Special Issue.
Guest-Editor, International Journal of Neural Systems, Vol. 15, no. 5, 2006 Special Issue.
Guest-Editor, Neural Networks, Vol. 15, nos. 8-9, 2002 Special Issue. Guest-editor of 2012 Special Issue on Advanced Computational Techniques and Tools for Neuroscience, for the Computational Intelligence and Neuroscience journal. submissions are now closed!

General/Program/Steering Committee Chair of the International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), an established conference on data analytics and learning paradigms and their applications. Its proceedings (published as LNCS series) have been ranked as one of the top 25% downloaded by the Springer.
IDEAL'18, Madrid, Spain; IDEAL'17, Guilin, China; IDEAL'16, Yangzhou, China; IDEAL'15, Wroclaw, Poland; IDEAL'14, Salamanca, Spain; IDEAL'12, Natal, Brazil; IDEAL'11, Norwich, UK; IDEAL'10, Paisley, Scotland, IDEAL'09, Burgos, Spain; IDEAL'08, Daejeon, South Korea; IDEAL'06, Burgos, Spain; IDEAL'05, Queensland, Australia.
Program Committee Chair of IDEAL'13, Hefei, China; IDEAL'07, Birmingham, UK); IDEAL'04, Exeter, UK; IDEAL'03, Hong Kong; IDEAL'02, Manchester, UK.

Program Committee Co-Chair, 2006 International Symposium on Neural Networks.
Co-Chair, Publication Committee, 2005 International Symposium on Neural Networks.
Co-Chair, Publication Committee, 2004 International Symposium on Neural Networks.
Member of Steering Committee, Workshop on Self-Organising Maps series.
Programme Committee members for a number of international conferences.

Current and Recent Research Projects

• Early Detection of Plant Virus Using Multispectral Imaging and Machine Learning
• Deep Learning Based Object Detection in Industrial Applications
• Multimanifold for Facial Expression Synthesis
• Deep Learning for Deformed and Degenerated Image Recognition
• Multimanifold and Multimodal Data Analysis
• Deep Learning for Face and General Image Recognition
• Robust Mobile Face Recognition with Healthcare Applications
• Multispectral Image Analysis and Classification with Applications
• Complex Temporal and Time Series Modelling and Predication
• Data Visualisation and Nonlinear Manifolds, Visualisation-induced SOM (ViSOM), Discrete Principal Curve/Surface
• Dimensionality Reduction for Face Recognition under Varying Illuminations
• Data Reduction Techniques for Information Quantification of Multiple Neural Responses (Spike Trains, LFPs)
• (Nonlinear) Independent Component Analysis and Blind Signal/Image Deconvolution and Denoising
• Fault-Tolerant and Bayesian Approaches to Self-Organising Neural Networks
• Finite Gaussian Mixtures or Mixture Models and Information Fusion
• Image Enhancement (esp. Blind Deblurring) and Retrieval
• Machine Learning in Mass Spectrometry
• Temporal Gene Expression and Time Series Analysis
• Text/Document Analysis and Organisation
• Kernel Methods and Fast Learning for Novelty Detection

Research Grants

Innovate UK KTP:
Real-time Embedded Deep Learning System for Hazardous Material Detection in Waste Recycling Streams (with Bensons Ltd.)
Deep Learning based Real-time Object Recognition and Volume Estimation from Degenerated Images Using Deep Learning (with KWM Ltd.), 2018-2020
EPSRC:
Digital Twin-based Bilateral Tele-autonomous System for Nuclear Remote Operation (EP/S03286X/1), 2019-2021 (with J. Carrasco)
Deep Learning based Sensor Signal Modelling for Enhanced Interpretation (EPSRC IAA-220), 2019
Reliable Face-based Authentication for Mobile Healthcare Applications (EPSRC IAA-058 with industrial input from eLucid Ltd.), 2014-2015
Data Reduction Techniques for Systematic Information Quantification in Large Scale, Multiple Spike Trains (EP/E057101/1), 2007-2008 (with S. Panzeri)
Workshop on Biologically Inspired Information Fusion (EP/E012795/1), 2006 (with M. Casey, A. Browne, P. Sowden)
Nonlinear ICA and Applications to Image/Video Noise Reduction (GR/R01460/01), 2001-2002 (with N. Allinson)
Emergent Behaviour Computing (GR/M56500/01), 1999-2000 (with N. Allinson, H. Bolouri, A. Holden, J. Austin, A. Jones)
Fault-tolerant and Bayesian approaches to self-organising neural networks (GR/M12889/01), 1998-2002 (with N. Allinson)
BBSRC:
Improved Identification of Proteins from Fragment Ion Spectra Using Machine Learning in Proteomics (EGM17685) , 2003-2006 (with S. Hubbard, S. Gaskell)
Integrative Systems Biology Centre, Manchester as one of the 40 Investigators in this large strategic £6.2M grant led by Douglas Kell, 2005-2011.
MIMIT
I-Patch: Development of gaming-based mobile monitoring treatment for childhood amblyopia, 2013-2014 (with T. Aslam and J. Ashworth)
Roy. Soc.
Travel Grant: International Conference on Artificial Neural Networks, Limassol, Cyprus. 2009
CitNOW Ltd.: Automatic Detection of Vehicle Logos and Face Recognition in Videos, 2015-2016
DTI and Premier Tech. Ltd (MTI): (KTA, formerly Teaching Company Scheme) Data Mining and Knowledge Management, 2000-2002 (with J. Keane, N. Allinson)
EPS Strategic Fund: Neural Techniques for Automatic Detecting Abnormal Patterns in Power System Operations, 2011-2012 (with J. Milanovic).

Research Students and Associates

Current PhD Students:
(Prospective studenst are welcome to contact me to discuss possible PhD projects)

• Yating Huang, working on Medical Image Analysis and Classification Using Deep Learning. 2022-2025.
  
• Qijun Yang, working on Fourier Ptychography with Deep Learning. 2021-2024.
  
• Lintao Xiang, working on Multiview Image reconstruction Using Deep Learning. 2021-2024.
  
• Qifan Zhou, working on Deep Learning for Microscopic Image Detection and Quantification. 2021-2024.
  
• Mengyuan Ma, working on Deep Learning for Object Detection. 2020-2023.
  
• Tajul Miftahushudur, working on Machine learning methods for feature selection and classification on unbalanced hyper/multi-spectral data in agriculture applications. 2020-2023.
  
• Halil Mertkan, working on Machine Learning for Hyper-/Multi-spectral Image Data Analysis. 2019-2023.
  

  Completed:

• Mengyu Liu, awarded PhD in 2022 with thesis on Deep Learning for Semantic Segmentation.
  
• Boyan Xu, awarded MPhil in 2022 with thesis on Deep Learning for Image Restoration.
  
• Jingwen Su, awarded PhD in 2022 with thesis on Deep Learning Based Approaches to Image Deblurring and Super-Resolution.
  
• Sebastian Flennerhag, (co-supervised with M. Elliot and J. Keane), awarded PhD in 2021 with thesis on Towards Scalable Meta-Learning.
  
• Ananya Gupta, awarded PhD in 2020 with thesis on Deep Learning for Semantic Feature Extraction in Aerial Imagery.
  
• Richard Hankins, awarded PhD in 2019 with thesis on Unsupervised Feature Learning for Convolutional Neural Networks, sponsored by EPSRC studentship.
  
• Yao Peng, awarded PhD in 2019 with thesis on Facial Expression Synthesis and Feature Learning for Convolutional Neural Networks.
  
• Ali Alsuwaidi, awarded PhD in 2018 with thesis on on Hyperspectral Image Analysis and Classification for e-Agri Applications.
  
• Shireen M. Zaki, awarded PhD in 2017 with thesis on A Multi-manifold Approach to Invariant Face Recognition.
  
• Jing Huo, working on Hetergeneous Face Recognition during 2015-2016 on leave from Nanjing University and was awarded PhD in 2017 at Nanjing.
  
• Yicun Ouyang, awarded PhD in 2016 with thesis on on Neural Networks for Finanical Time Series Modelling and Prediction. He works now as a post-doc in Shenzhen.
  
• James Burstone, awarded PhD in 2014 with thesis on Generative Modles for Robust Face Recognition. He is now the CTO of eLucid mHealth Ltd.
  
• Aftab Khan, awarded PhD in 2013 with thesis on Efficient Methodologies for Single-Image Blind Deconvolution and Deblurring.
  
• Weilin Huang, awarded PhD in 2013 with thesis on Robust Facial Representation for Recognition. Now he works at Oxford University.
  
• Zareen Mehboob, awarded PhD in 2011 with thesis on Information Theoretic Neural Information Processing. she is now an information analyst at NHS Surrey.
  
• He Ni, awarded PhD in 2008 with thesis on on Self-Organising Local Regressive Models for Nonstationary Financial Time-Series; and now works at Zhejiang Gongshang University.
  
• Israr Hussain, awarded PhD in 2008 with the thesis on Non-Gaussianity Based Image Deblurring and Denoising.
  
• Shireen Md Zaki, awarded MPhil in 2008 with thesis title: Growing Self-Organising Networks for Face Recognition.
  
• Amber Lei Wang Clifton, awarded PhD in 2007 with the thesis on Novelty Detection and Fusion of Classifiers; and now works at Oxford University.
  
• Carla Möller-Levet, awarded PhD in 2005 with the thesis on Clustering Algorithms for Microarray Data; and then worked at the Paterson Institute for Cancer Research. Now she works at University of Surrey.
  
• Swapna Sarvesvaran, awarded MPhil in 2005 with the thesis on Data Analysis and Visualisation Methods for Gene Expressions; and now works for the Centrica.
  
• Richard Freeman, awarded PhD in 2004 with the thesis on Text/Document Mining and Management Using Neural Networks; and now works at the Capgemini.
  

  Post-Doc RAs:

• Richard Hankins, working on Kenny Waste KTP project (2018-2021).
• Phil Worthington, working on Bensons KTP project (2019-2022).
• Yao Peng, working on Cassava virus early detection project (Gates Foundation) (2019-2020).
• James Burstone, working on Reliable Mobile Face Recognition App (2014-2015).
• King-Wai Lau, working on Machine Learning for Protein Sequence from Mass Spectrometry, a joint project with Biomolecular Sciences Department (School of Life Sciences) (2003-2006)
  
• Michel Haritopoulos, worked on Nonlinear ICA and Image Denoising (2001-2003)
  
• Qingfu Zhang, worked on ICA and SOMs (1999-2001) and is now at University of Essex.
  

Publications (selected)

2022:

• J. Su, B. Xu, H. Yin, "A survey of deep learning approaches to image restoration," Neurocomputing, vol. 487, 46-65, 2022.
• Y. Mao, S. Zhong, H. Yin, "Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy," Physics of Fluids, vol. 34(5), 053602, 2022.
• J. Huo, X. Liu, W. Li, Y. Gao, H. Yin, J. Luo, "CAST: Learning Both Geometric and Texture Style Transfers for Effective Caricature Generation," IEEE Trans. on Image Processing, vol. 31, pp. 3347-3358, 2022.
• Y. Peng, M.M. Dallas, J.T. Ascencio-Ibáñez, J.S. Hoyer, J. Legg, L. Hanley-Bowdoin, B. Grieve, H. Yin, "Early detection of plant virus infection using multispectral imaging and spatial–spectral machine learning," Scientific Reports, vol. 12(1), pp. 1-14, 2022.

  2021:

• A. Gupta, S. Watson, H. Yin, "Deep learning-based aerial image segmentation with open data for disaster impact assessment," Neurocomputing, vol. 439, 22-33, 2021.
• M. Liu, H. Yin, "Efficient pyramid context encoding and feature embedding for semantic segmentation," Image and Vision Computing, vol. 111, pp. 104195, 2021.
• A. Khan, H. Yin, "Arbitrarily shaped point spread function (PSF) estimation for single image blind deblurring," The Visual Computer, vol. 37(7), pp. 1661-1671, 2021.

  2020:

• Q. Tian, M. Cao, S. Chen, H. Yin, "Structure-exploiting discriminative ordinal multioutput regression," IEEE Trans. on Neural Networks and Learning Systems, vol. 32(1), 266-280, 2020.
• Y. Peng, R. Hankins, H. Yin, "Data-independent feature learning with Markov random fields in convolutional neural networks," Neurocomputing, vol. 378(22), pp. 24-35, 2020.
• Q. Tian, W. Zhang, J. Mao, H. Yin, "Real-time human cross-race aging-related face appearance detection with deep convolution architecture," Journal of Real-Time Image Processing, vol. 17(1), pp. 83-93, 2020.
• S. Flennerhag, A.A. Rusu, R. Pascanu, F. Visin, H. Yin, R. Hadsell, "Meta-learning with warped gradient descent," ICLR 2020.

  2019:

• A. Gupta, J. Byrne, D. Moloney, S. Watson, H. Yin, "Tree annotations in LiDAR data using point densities and convolutional neural networks," IEEE Trans on Geoscience and Remote Sensing, vol. 58(2), pp. 971-981, 2019.
• Y. Peng, H. Yin, "ApprGAN: Appearance-based generative adversarial network for facial expression synthesis," IET Image Processing, vol. 13(14), pp. 2706-2715, 2019.
• C. Veys, F. Chatziavgerinos, A. AlSuwaidi, J. Hibbert, M. Hansen, G. Bernotas, M. Smith, H. Yin, S. Rolfe, B. Grieve, "Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape," Plant Methods, vol. 15:4, pp.1-12, 2019.
• B.D. Grieve, T. Duckett, M. Collison, L. Boyd, J. West, H. Yin, F. Arvin, S. Pearson, "The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required," Global Food Security, vol. 23, pp. 116-124, 2019.
• Q. Tian, M. Cao, S. Chen, H. Yin, "Relationships self-learning based gender-aware age estimation," Neural Processing Letters, vol. 50, pp. 2141-2160, 2019.

  2018:

• A. AlSuwaidi, B. Grieve, H. Yin, "Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(4), pp. 1041-1055, 2018.
• Y. Peng, H. Yin, "Facial expression analysis and expression-invariant face recognition by manifold based synthesis," Machine Vision and Applications, Vol. 29(2), pp. 263-284, 2018.
• X. Chen, H. Yin, F. Jiang, L. Wang, "Multi-view dimensionality reduction based on Universum learning," Neurocomputing, Vol.275, pp. 2279-2286, 2018.
• J. Huo, Y. Gao, Y. Shi, W. Yang, H. Yin, "Heterogeneous face recognition by margin-based cross-modality metric learning," IEEE Trans. on Cybernetics, Vol. 48(6), pp. 1814-1826, 2018.
• Y. Ouyan, H. Yin, "Multi-step time series forecasting with an ensemble of varied length mixture models," Int. J. Neural Systems, Vol. 28(4), pp.1750053, 2018.
• J. Huo, Y. Gao, Y. Shi, H. Yin, "Cross-modal metric learning for AUC optimization," IEEE Trans. on Neural Networks and Learning Systems, vol 48(6), pp. 1814-1826, 2018.
• S. Flennerhag, H. Yin, J. Keane, M. Elliot, "Breaking the activation function bottleneck through adaptive parameterization," NeurIPS, 2018.

  2017:

• W. Huang, H. Yin, "Robust face recognition with structural binary gradient patterns," Pattern Recognition, Vol. 68, pp. 126-140, 2017.
• Y. Peng, H. Yin, "Markov random field based convolutional neural networks for image classification," Proc. of Int. Conf. on Intelligent Data Analysis and Automated Learning (IDEAL), pp. 387-396, 2017.
• A. AlSuwaidi, C. Veys, M. Hussey, B. Grieve, H. Yin, "Spectral-texture approach to hyperspectral image analysis for plant classification with SVMs," Proc. IEEE IST, DOI: 10.1109/IST.2017.8261496, 2017.
• J. Huo, Y. Gao, Y. Shi, H. Yin, "Variation robust cross-modal metric learning for caricature recognition," Proc. of Thematic Workshops of ACM Multimedia, pp.340-348, 2017.

  2016:

• A. AlSuwaidi, C. Veys, M. Hussey, B. Grieve, H. Yin, "Hyperspectral selection based algorithm for plant classification," Proc. IEEE IST, pp. 395-400, 2016.
• Y. Peng, H. Yin, "Expression classification and intensity estimation by expression manifold synthesis," Proc. of Int. Conf. on Intelligent Data Analysis and Automated Learning (IDEAL), pp. 635-644, 2016.
• J. Huo, Y. Gao, Y. Shi, W. Yang, H. Yin, "Ensemble of sparse cross-modal metrics for heterogeneous face recognition," Proc. of ACM Multimedia, pp.1405-1414, 2016.

  2014:

• A. Clifton, D.A. Clifton, Y. Zhang, P. Watkinson, L. Tarassenko, H. Yin, "Probabilistic novelty detection with support vector machines," IEEE Trans. on Reliability, Vol.63(2), pp. 455-467, 2014.
• J. Huo, Y. Gao, W. Yang, H. Yin, "Multi-Instance dictionary learning for detecting abnormal events in surveillance videos," Int. J. Neural Syst. Vol. 24(3), 2014.
• Y. Ouyang, H. Yin, "A neural gas mixture autoregressive network for modelling and forecasting FX time series," Neurocomputing, Vol.135, pp. 171-179, 2014.

  2012:

• W. Huang and H. Yin, “On nonlinear dimensionality reduction for face recognition,” Image and Vision Computing, vol. 30(4-5), pp. 355-366, 2012.

  2011:

• J. Burnstone and H. Yin, "Eigenlights: recovering illumination from face images," IDEAL 2011, pp. 490-497, 2011.
• A. Khan and H. Yin, "Spectral non-gaussianity for blind image deblurring," IDEAL 2011, pp. 144-151, 2011.
• Z. Mehboob and H. Yin, “Information quantification of empirical mode decomposition and applications to field potentials,” Int. J. Neural Syst., vol. 21(1), pp. 49-63, 2010.
• B. Baruque, E. Corchado and H. Yin, "The S2-ensemble fusion algorithm," Int. J. Neural Syst., vol. 21(6), pp. 505-525, 2011.
• H. Yin "Advances in adaptive nonlinear manifolds and dimensionality reduction," Frontiers of Electrical and Electronic Engineering in Chinas, Vol. 6(1), pp. 72-85, 2011.

  2010:

• H. Yin and W. Huang, "Adaptive nonlinear manifolds and their applications to pattern recognition," Information Sciences, Vol. 180(14), pp. 2649-2662, 2010.
• W. Huang and H. Yin, “A dissimilarity kernel with local features for robust facial recognition,” ICIP 2010, pp. 3785-3788, 2010.

  2009:

• H. Yin, "Dimensionality reduction and manifold learning via topological mappings," Neural Networks Tri-Society Newsletters, Vol. 7(2), pp. 7-9, 2009.
• H. Ni and H. Yin, “Exchange rate prediction using hybrid neural networks and trading indicators,” Neurocomputing, , Vol. 72, pp. 2815-2823, 2009.
• H. Ni and H. Yin, “A self-organising mixture autoregressive network for FX time series modelling and prediction,” Neurocomputing, , Vol. 72, pp. 3529-3537, 2009.
• Z. Mehboob and H. Yin, “Information preserving empirical mode decomposition for filtering field potentials,” IDEAL 2009, pp. 226-233, 2009.
• W. Huang and H. Yin, “ViSOM for dimensionality reduction in face recognition,” WSOM 2009, pp. 107-115, 2009.

  2008:

• H. Ni and H. Yin, "Self-organising mixture autoregressive model for non-stationary time series modelling," International Journal of Neural Systems, Vol. 18, pp. 469-480, 2008.
• S.M. Zaki and H. Yin, "A semi-supervised learning algorithm for growing neural gas in face recognition," Journal of Mathematical Modelling and Algorithms, vol. 7, pp. 425-435, 2008.
• H. Yin, "On multidimensional scaling and embedding of self-organising maps," Neural Networks, Vol. 21, pp. 160-169, 2008.
• H. Yin and I. Hussain, "Independent component analysis and nongaussianity for blind image deconvolution and deblurring," Journal of Integrated Computer-Aided Engineering, vol. 15, pp. 219-228, 2008.
• H. Yin, S. Panzeri, Z. Mehboob, M.E. Diamond, "Decoding Population Neuronal Responses by Topological Clustering," Proc. Int. Conf. on Artificial Neural Networks (ICANN’08), Vol. II, 547-556, 2008.
• H. Yin, "Self-organising maps: Background, theories, extensions and applications (Invited Book Chapter)," Computational Intelligence: A Compendium, Springer, 715-762, 2008.

  2007:

• H Yin, "Nonlinear dimensionality reduction and data visualisation: A review," International Journal of Automation and Computing, Vol. 4, pp. 294-303, 2007.
• H Yin, "Chapter 3: Learning nonlinear principal manifolds by self-organising maps (invited chapter)," A.N. Gorban, B. Kegl, D.C. Wunsch, A. Zinovyev (eds.) Principal Manifolds for Data Visualization and Dimension Reduction, pp. 68-95, 2007.
• L.A. Clifton, H. Yin, D.A. Clifton and Y. Zhang, , "“Combined support vector novelty detection for multi-channel combustion data," Proc. IEEE ICNSC’07, pp. 495-500, 2007.
• H Ni and H Yin, "Time-series prediction using self-organising mixture autoregressive network," Proc. IDEAL’07, , LNCS-4881, pp. 1000-1009, 2007.
• H Yin, "Connection between self-organising maps and metric multidimensional scaling," Proc. IJCNN’07, pp. 1025-1030, 2007.

  2006:

• H Yin, "On the equivalence between kernel self-organising maps and self-organising mixture density networks," Neural Networks, Vol. 19, pp. 780-784, 2006.
• KW Lau, H Yin and S Hubbard, "Kernel self-organising maps for classification," Neurocomputing, vol. 69, pp. 2033-2040, 2006.
• T McLaughlin, J Siepen, J Selley, JA Lynch, KW Lau, H Yin, S Gaskell, and S Hubbard, "PepSeeker: A database of proteome peptide identifications for investigating fragmentation patterns," Nucleic Acids Research, Vol. 34, pp. D649-D654, 2006.
• H Yin, “Introduction to learning algorithms - Editorial,” Journal of Mathematical Modelling and Algorithms, vol. 5(4), pp. 395-544, 2006.
• LA Clifton, H Yin and Y Zhang, "Support vector machine in novelty detection for multi-channel combustion data," Proc. Int. Symposium on Neural Networks (ISNN’06), Vol. III, 836-843, 2006.
• H Ni and H Yin, "Recurrent self-organising maps and local support vector machine models for exchange rate prediction," Proc. Int. Symposium on Neural Networks (ISNN’06), Vol. III, 504-511, 2006.

  2005:

• C Möller-Levet and H Yin, "Modelling and analysis of gene expression time-series based on co-expression," International Journal of Neural Systems, Special Issue on Bioinformatics, Vol. 15(4), pp. 311-322, 2005.
• R Freeman and H Yin, "Web content management by self-organisation," IEEE Trans. on Neural Networks, Special Issue on Adaptive Learning Systems in Communication Networks, Vol. 16(5), pp. 1256-1268, 2005.
• C S Möller-Levet, F Klawonn, K-H Cho, H Yin, and O Wolkenhauer, "Clustering of unevenly sampled gene expression time-series data," Fuzzy Sets and Systems, Vol. 152(1), pp. 49-66, 2005.
• R Freeman and H Yin, "Tree view self-organisation of web content," Neurocomputing, Vol. 63, pp. 415-446, 2005.
• Y Hao, J Liu, Y Wang, Y-M Chueng, H Yin, L Jiao, J Ma and Y-C Jiao (2005). Computational Intelligence and Security, Springer-Verlag: Berlin, ISBN 3-540-30818-0.
• H Yin, "Self-organising map as a natural kernel method (invited paper)," Proc. Int. Conf. on Neural Networks and Brain (ICNN&B’05), 1891-1894, IEEE Press 2005.
• B Li and H Yin, "Face recognition using support vector machine fusion and wavelet transform," Proc. Int. Conf. on Computational Intelligence and Security (CIS’05), Vol. II, 764-771, 2005.
• KW Lau, J Lynch, T MacLaughlin, J Lovric, B Stapely, S Gaskell, H Yin, and S Hubbard, "Improved identification of proteins from fragment ion spectra using machine learning in proteomics," Proc. ASAM’05, 2005.
• H Yin and KW Lau, "Kernel SOMs and mixture networks,” Proc. WSOM’05, 421-427, 2005.
• B Li and H Yin, "Face recognition using RBF neural networks and wavelet transform," Proc. Int. Symposium on Neural Networks (ISNN’05), vol.II, 105-111, 2005.
• KW Lau, B Stapley, S Hubbard and H Yin, "Matching peptide sequences with mass spectra," Proc. Int. Conf. on Intelligent Data Engineering and Automated Learning (IDEAL’05), 391-397, 2005.
• CS Möller-Levet and H Yin, "Circular SOM for temporal characterisation of modelled gene expressions," Proc. Int. Conf. on Intelligent Data Engineering and Automated Learning (IDEAL’05), 319-326, 2005.

  2004:

• R Freeman and H Yin, "Adaptive topological tree structure (ATTS) for document organisation and visualisation," Neural Networks, Vol. 17, pp. 1255-1271, 2004.
• Z Yang, R Everson and H Yin (Eds.), Intelligent Data Engineering and Automated Learning (IDEAL), Lecture Notes in Computer Science 3177,, Springer: Berlin, 2004.
• C S Möller-Levet, H Yin, K-H Cho, and O Wolkenhauer, "Modelling gene expression time-series with radial basis function neural networks," in Proc. IJCNN'04, pp. 1191-1196, 2004.
• C S Möller-Levet and H Yin, "Modelling and clustering of gene expressions using RBFs and a shape similarity metric," in Lecture Notes in Computer Science 3177, pp. 1-10, 2004.
• R Freeman and H Yin, "Topological tree for web organisation, discovery and exploration," Lecture Notes in Computer Science 3177, pp. 478-484, 2004.
• S Sarvesvaran and H Yin, "Visualisation of distributions and clusters using ViSOMs on gene expression data," in Lecture Notes in Computer Science 3177, pp. 78-84, 2004.

  2003:

• J Liu, Y Cheung, and H Yin (Eds.), Intelligent Data Engineering and Automated Learning (IDEAL), Lecture Notes in Computer Science 2690, Springer: Berlin, 2003.
  
• H Yin, "Resolution enhancement for the ViSOM, " in Proc. WSOM'03, pp. 208-212, 2003.
• H Yin, "Nonlinear multidimensional data projection and visualisation," in Lecture Notes in Computer Science 2690, pp. 377-388, 2003.

  2002:

• N M Allinson, K Obermayer and H Yin (Eds.), Neural Network, Vol 15, Issues 8-9, 2002 Special Issue: New Developments in Self-Organising Maps., 2002.
  
• H Yin, N M Allinson, R Freeman, J Keane, and S Hubbard (Eds.),Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science 2412, Springer: Berlin, 2002.
  
• H Yin, "ViSOM - A novel method for multivariate data projection and structure visualisation," IEEE Trans. on Neural Networks, Vol. 13, No. 1, pp. 237-243, 2002.
• H Yin, "Data visualisation and manifold mapping using ViSOM," Neural Networks, Vol. 15, pp. 1005-1016, 2002.
• M Haritopoulos, H Yin and N Allinson, "Image denoising using SOM-based nonlinear independent component analysis," Neural Networks, Vol. 15, pp. 1085-1098, 2002.
• R Freeman, H Yin, and N Allinson, "Self-organising maps for tree view based hierarchical document clustering,"" in Proc. IJCNN 2002, pp. 1906-1911, 2002.
• M Haritopoulos, H Yin and N Allinson, Self-organising map applied to image denoising," Proc. IEEE Workshop on Neural Networks for Signal Processing, pp. 525-534, 2002.
• R Freeman and H Yin, "Self-organising maps for hierarchical tree view document clustering using contextual information,"" in Lecture Notes in Computer Science 2412, pp. 123-128, 2002.
• B Russell, H Yin and N M Allinson, "Document clustering using 1 + 1 dimensional Self-organising map, " in Lecture Notes in Computer Science 2412, pp. 123-128, 2002.

  2001:

• N Allinson, H Yin, L Allinson and J Slack (Eds), Advances in Self-Organising Maps,, Springer: London, 2001.
• H Yin and N M Allinson, " Self-organising mixture networks for probability density estimation," IEEE Trans. on Neural Networks, Vol. 12, No. 2, pp. 405-411, 2001.
• H Yin and N M Allinson, " A Bayesian self-organising map for Gaussian mixtures," IEE Proc.- Vision, Image and Signal Processing, Vol. 148, No. 4, pp. 234-240, 2001.
• M Haritopoulos, H Yin and N Allinson, " Multiplicative noise removal using self-organising maps," in Proc. Int. Conf. on Independent Component Analysis, 2001, pp. 206-211.
• H Yin, " Visualisation induced SOM (ViSOM)," in Advances in Self-Organising Maps, Springer, 2001, pp. 81-88.
• M Haritopoulos, H Yin and N Allinson, "Nonlinear blind source separation using SOMs and applications to image denoising," in Advances in Self-Organising Maps, Springer, 2001, pp. 275-282.

  2000:

• Q Zhang, H Yin and N Allinson, "A simplified ICA based denoising methods, " in Proc. Int. Joint. Conf. on Neural Networks, Vol. 5, pp 479-482, 2000.
• Q Zhang, N Allinson and H Yin, "Population optimisation algorithm based ICA, " in Proc. The First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, pp. 24-31, 2000.

  1999:

• H Yin and N M Allinson, " Interpolating self-organising maps (iSOM)," Electronics Letters, Vol. 35, No. 19, pp. 1649-1650, 1999.
• H Yin and N M Allinson, " Averaging ensembles of self-organising mixture networks for density estimation," in Proc. Int. Joint Conf. on Neural Networks (IJCNN'99), Vol. 2, pp. 1456-1460, 1999.
• N M Allinson and H Yin, " Self-organising maps for pattern recognition," in Kohonen Maps, E. Oja and S. Kaski (Eds.), Elsevier, 1999, pp. 111-120.

  1998:

• H Yin and N M Allinson. "A self-organising mixture network for density modelling," in Proc. IEEE Int. Joint Conf. on Neural Networks, Vol. 3, pp. 2277-2281, 1998.

  1997:

• H Yin and N M Allinson. "Bayesian learning for self-organising maps," Electronics Letters, Vol. 33, No. 4, pp. 304-305, 1997.
• H Yin and N M Allinson. "Comparison of a Bayesian SOM with the EM algorithm for Gaussian mixtures," in Proc. Workshop on Self-Organising Maps (WSOM'97), pp. 118-123, 1997.
• N M Allinson and H Yin, Part G 1.1 Unsupervised segmentation of textured images, in Handbook of Neural Computation, E. Fiesler and R. Beale (Eds.) A Joint Publication of The Institute of Physics Publishing and Oxford University Press, 1997.

  1996:

• H Yin and N M Allinson. "An equidistortion principle constrained SOM for vector quantisation," in Proc. Int. Conf. on Neural Inform. Processing (ICONIP'96), Vol. 1, pp. 80-83, 1996.

  1995:

• H Yin and N M Allinson. " On the distribution and convergence of the feature space in self-organising maps," Neural Computation , Vol. 7, No. 6, pp. 1178-1187, 1995.
• H Yin and N M Allinson. "Towards the optimal Bayes classifier using an extended self-organising map," in Proc Int. Conf. on Artificial Neural Networks (ICANN'95), Vol. 2, pp. 45-49, 1995.

  1994:

• H Yin and N M Allinson. "Unsupervised segmentation of textured images using a hierarchical neural structure," Electronics Letters, Vol. 30, No. 22, pp. 1842-1843, 1994.
  
• H Yin and N M Allinson. "Self-organised parameter estimation and segmentation of MRF model-based texture images," in Proc IEEE Int. Conf. on Image Processing(ICIP'94), Vol. 2, pp. 645-649, 1994.
• H Yin and N M Allinson. "Self-organised segmentation of textured images," in Proc Int. Conf. on Artificial Neural Networks (ICANN'94), Vol. 2, pp. 1149-1152, 1994.

  1993:

• H Yin and N M Allinson. "On the distribution of feature space in self-organising mapping and convergence accelerating by a Kalman algortihm," in New Trends in Neural Computation, J. Mira, J. Cabestany and A. Prieto (Eds.), Springer, 1993, pp. 291-296.
• H Yin and N M Allinson. "Stochastic analysis and comparison of Kohonen SOM with optimal filters," In Proc. The Third IEE Int. Conf. on Artificial Neural Networks, pp. 182-185, 1993.