Research Interests

Teaching Responsibilities

Recent Activities

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 Co-Chair of IDEAL 2012, IDEAL 2011, IDEAL 2010, IDEAL 2009.

Plenary Speaker at 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.

Associate Editor, IEEE Transactions on Neural Networks (2006-2009).
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.

Program Committee Chair, 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007).
Program Committee Co-Chair, 2006 International Symposium on Neural Networks.
General Co-Chair, The Seventh International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006).
General Co-Chair, The Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005).
Co-Chair, Programme Committee, The Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004).
Co-Chair, Programme Committee, The Fourth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2003).
Chair, Organising Committee, The Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2002).
Co-Chair, Steering Committee, IDEAL conference series.
Member, Steering Committee, Workshop on Self-Organising Maps series.
Co-Chair, Publication Committee, 2005 International Symposium on Neural Networks.
Co-Chair, Publication Committee, 2004 International Symposium on Neural Networks.
Programme Committee members for a number of international conferences.

Current and Recent Research Projects

• 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 Students and Associates

Current PhD Students:

• Yicun Ouyang, working on Finanical Time Series Modelling and Prediction. 2011-2014.
  
• James Burnstone, working on Face Recognition on Mobile Systems. 2010-2013.
  
• Aftab Khan, working on Advanced Blind Image deblurring Techniques. 2010-2013.
  
• Amr Alkhuffash, working on Large Scale, Distributed Image Retrieval Systems. 2008-2011.
  
• Weilin Huang, working on Kernel Based Methods for Face Recognition. 2008-2011.
  
  

  Completed:

• Zareen Mehboob, awarded PhD in 2011 with thesis on Information Theoretic Neural Information Processing.
  
• He Ni, awarded PhD in 2008 with thesis on on Self-Organising Local Regressive Models for Nonstationary Financial Time-Series; and now a lecturer 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 now works at the Paterson Institute for Cancer Research.
  
• 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:

• 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) and is now with the School of Life Sciences.
  
• 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)

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.