Uncorrelated slow feature discriminant analysis using globality preserving projections for feature extraction

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method for classification inspired by biological mechanism. However, SFDA only considers the local geometrical structure information of data and ignores the global geometrical structure information. Furthermore, previous works have demonstrated that uncorrelated features of minimum redundancy are effective for classification. In this paper, a novel method called uncorrelated slow feature discriminant analysis using globality preserving projections (USFDA-GP) is proposed for feature extraction and recognition. In USFDA-GP, two kinds of global information are imposed to the objective function of conventional SFDA for respecting some more global geometric structures. We also provide an analytical solution by simple eigenvalue decomposition to the optimal model instead of previous iterative method. Experimental results on Extended YaleB, CMU PIE and LFW-a face databases demonstrate the effectiveness of our proposed method.

[1]  Quanxue Gao,et al.  Joint Global and Local Structure Discriminant Analysis , 2013, IEEE Transactions on Information Forensics and Security.

[2]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Jieping Ye,et al.  Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Quan-Sen Sun,et al.  Multiset Canonical Correlations Using Globality Preserving Projections With Applications to Feature Extraction and Recognition , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Mei Tian,et al.  Slow Feature Discriminant Analysis and its application on handwritten digit recognition , 2009, 2009 International Joint Conference on Neural Networks.

[6]  Daoqiang Zhang,et al.  Efficient and robust feature extraction by maximum margin criterion , 2003, IEEE Transactions on Neural Networks.

[7]  Fumin Shen,et al.  Fast Approximate L_infty Minimization: Speeding Up Robust Regression , 2013, ArXiv.

[8]  Weiguo Gong,et al.  Uncorrelated linear discriminant analysis based on weighted pairwise Fisher criterion , 2007, Pattern Recognit..

[9]  Jian-Huang Lai,et al.  Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition , 2013, Neural Networks.

[10]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[11]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[12]  YangJian,et al.  Two-Dimensional PCA , 2004 .

[13]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[14]  Dacheng Tao,et al.  Slow Feature Analysis for Human Action Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Xingjian Gu,et al.  Dimensionality Reduction Based on Supervised Slow Feature Analysis for Face Recognition , 2014 .

[16]  Mei Tian,et al.  Nonlinear dimensionality reduction using a temporal coherence principle , 2011, Inf. Sci..

[17]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[18]  J KriegmanDavid,et al.  Eigenfaces vs. Fisherfaces , 1997 .

[19]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[20]  Xiaolong Teng,et al.  Face recognition using discriminant locality preserving projections , 2006, Image Vis. Comput..

[21]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[22]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[23]  Dong Xu,et al.  Trace Ratio vs. Ratio Trace for Dimensionality Reduction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Tal Hassner,et al.  Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Zhong Jin,et al.  Face recognition using discriminant locality preserving projections based on maximum margin criterion , 2010, Pattern Recognit..

[27]  Wolfgang Konen,et al.  Gesture recognition on few training data using Slow Feature Analysis and parametric bootstrap , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[28]  Chris H. Q. Ding,et al.  Pairwise-Covariance Linear Discriminant Analysis , 2014, AAAI.

[29]  Matti Pietikäinen,et al.  Learning Discriminant Face Descriptor , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Limei Zhang,et al.  Graph-optimized locality preserving projections , 2010, Pattern Recognit..

[31]  Jing-Yu Yang,et al.  Learning image manifold via local tensor subspace alignment , 2014, Neurocomputing.

[32]  David Zhang,et al.  Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms , 2011, 2011 18th IEEE International Conference on Image Processing.

[33]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[34]  Niko Wilbert,et al.  Slow feature analysis , 2011, Scholarpedia.

[35]  Sheng Wang,et al.  Supervised Slow Feature Analysis for Face Recognition , 2013, CCBR.

[36]  Jun Guo,et al.  Learning a locality discriminating projection for classification , 2009, Knowl. Based Syst..

[37]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[38]  Zhong Jin,et al.  Global Sparse Representation Projections for Feature Extraction and Classification , 2009, 2009 Chinese Conference on Pattern Recognition.

[39]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[40]  Wolfgang Maass,et al.  Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks , 2009, NIPS.

[41]  Niko Wilbert,et al.  Invariant Object Recognition with Slow Feature Analysis , 2008, ICANN.

[42]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[43]  Jing-Yu Yang,et al.  A theorem on the uncorrelated optimal discriminant vectors , 2001, Pattern Recognit..

[44]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Lei Zhang,et al.  Feature extraction using fuzzy inverse FDA , 2009, Neurocomputing.

[46]  YanShuicheng,et al.  Graph Embedding and Extensions , 2007 .

[47]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[48]  Feiping Nie,et al.  Globally and Locally Consistent Unsupervised Projection , 2014, AAAI.

[49]  Josef Kittler,et al.  Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[50]  Jing-Yu Yang,et al.  Two-dimensional color uncorrelated discriminant analysis for face recognition , 2013, Neurocomputing.

[51]  Robert P. W. Duin,et al.  Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[53]  Di Zhang,et al.  Global plus local: A complete framework for feature extraction and recognition , 2014, Pattern Recognit..

[54]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).