Enhanced supervised locally linear embedding

In this paper, a new nonlinear dimensionality reduction algorithm, called enhanced supervised locally linear embedding (ESLLE), is proposed. The ESLLE method attempts to make the interclass dissimilarity definitely larger than the intraclass dissimilarity in an effort to strengthen the discriminating power and generalization ability of embedded data representation. Simulation studies on artificial manifold data demonstrate that ESLLE can give better embedding results in dimensionality reduction and is more robust to noise in comparison with the original supervised LLE (SLLE). Experimental results on extended Yale face database B and CMU PIE face databases demonstrate that ESLLE obtains better performance on face recognition compared with other famous methods such as principal component analysis (PCA), locally linear embedding (LLE) as well as SLLE.

[1]  I. Jolliffe.  Principal Component Analysis , 2002 .

[2]  Abdullah Al Mamun,et al.  Weighted locally linear embedding for dimension reduction , 2009, Pattern Recognit..

[3]  Olga Kayo,et al.  Locally linear embedding algorithm: extensions and applications , 2006 .

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

[5]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding Algorithm for Pattern Recognition , 2003, IbPRIA.

[6]  C. Viroli,et al.  Supervised locally linear embedding for classification : an application to gene expression data analysis Supervised locally linear embedding in problemi di classificazione : un ’ applicazione all ’ analisi di dati di espressione genica , 2005 .

[7]  Clara Pizzuti,et al.  Fast Outlier Detection in High Dimensional Spaces , 2002, PKDD.

[8]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding , 2003, ICANN.

[9]  Dong Liang,et al.  A facial expression recognition system based on supervised locally linear embedding , 2005, Pattern Recognit. Lett..

[10]  Meng Wang,et al.  SLLE for predicting membrane protein types. , 2005, Journal of theoretical biology.

[11]  Dimitrios Gunopulos,et al.  Non-linear dimensionality reduction techniques for classification and visualization , 2002, KDD.

[12]  Zhenyue Zhang,et al.  Probability-Based Locally Linear Embedding for Classification , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[13]  Dit-Yan Yeung,et al.  Robust locally linear embedding , 2006, Pattern Recognit..

[14]  John K. Tsotsos,et al.  Parameterless Isomap with Adaptive Neighborhood Selection , 2006, DAGM-Symposium.

[15]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[16]  D. D. Ridder,et al.  Locally linear embedding for classification , 2002 .

[17]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[18]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[19]  Nanning Zheng,et al.  Neighborhood Discriminant Projection for Face Recognition , 2006, International Conference on Pattern Recognition.

[20]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[21]  De-Shuang Huang,et al.  Locally linear discriminant embedding: An efficient method for face recognition , 2008, Pattern Recognit..

[22]  Changbo Hu,et al.  Manifold of facial expression , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[23]  Robert P. W. Duin,et al.  On Using a Pre-clustering Technique to Optimize LDA-Based Classifiers for Appearance-Based Face Recognition , 2007, CIARP.

[24]  Zhi-Hua Zhou,et al.  Supervised nonlinear dimensionality reduction for visualization and classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

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

[27]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.