Transposed Low Rank Representation for Image Classification

This paper proposes a method for supervised classification using Low-Rank Representation of transposed data. Recent papers have suggested that low rank representation of transposed data may be useful for feature extraction. We develop an algorithm called TLRRC for supervised classification using transposed data and demonstrate that its performance is competitive with state-of-the-art classification methods.

[1]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Yong Yu,et al.  Robust Subspace Segmentation by Low-Rank Representation , 2010, ICML.

[4]  Xiaogang Wang,et al.  A unified framework for subspace face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

[7]  Zhixun Su,et al.  Fixed-rank representation for unsupervised visual learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Xuegong Zhang,et al.  A PCA classifier and its application in vehicle detection , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[9]  David Zhang,et al.  Collaborative Representation based Classification for Face Recognition , 2012, ArXiv.

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

[11]  John Wright,et al.  Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.

[12]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[13]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[14]  David J. Kriegman,et al.  Clustering appearances of objects under varying illumination conditions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[16]  Takeo Kanade,et al.  A Multibody Factorization Method for Independently Moving Objects , 1998, International Journal of Computer Vision.

[17]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[18]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Hans-Peter Kriegel,et al.  Subspace clustering , 2012, WIREs Data Mining Knowl. Discov..

[20]  Shuicheng Yan,et al.  Latent Low-Rank Representation for subspace segmentation and feature extraction , 2011, 2011 International Conference on Computer Vision.

[21]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[22]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[23]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.