Extension of Sample Dimension and Sparse Representation Based Classification of Low-Dimension Data

As we know, sparse representation methods can achieve high accuracy for classification of high-dimensional data. However, they usually show poor performance in performing classification of low-dimensional data. In this paper, the increase of the sample dimension for sparse representation is studied and surprising accuracy improvement is obtained. The paper has the following main value. First, the designed method obtains promising results for classification of low-dimensional data and is very useful for widening the applicability of sparse representation. The accuracy of the designed method may be 10% higher than that of sparse representation based on original samples. To our knowledge, no similar work is available. Second, the designed method is simple and has a low computational cost. Extensive experiments are conducted and the experimental results also show that the designed method can be applied to improve other methods too.

[1]  Yong Xu,et al.  Feature space-based human face image representation and recognition , 2012 .

[2]  Xin Li,et al.  Image Recovery Via Hybrid Sparse Representations: A Deterministic Annealing Approach , 2011, IEEE Journal of Selected Topics in Signal Processing.

[3]  Bob Zhang,et al.  Multiple representations and sparse representation for image classification , 2015, Pattern Recognit. Lett..

[4]  Zhong Jin,et al.  Kernel sparse representation based classification , 2012, Neurocomputing.

[5]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[6]  David Zhang,et al.  A fast kernel-based nonlinear discriminant analysis for multi-class problems , 2006, Pattern Recognit..

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

[8]  Li Zhang,et al.  Kernel sparse representation-based classifier ensemble for face recognition , 2013, Multimedia Tools and Applications.

[9]  Liang-Tien Chia,et al.  Kernel Sparse Representation for Image Classification and Face Recognition , 2010, ECCV.

[10]  Qinghua Hu,et al.  Kernel sparse representation for time series classification , 2015, Inf. Sci..

[11]  Xuelong Li,et al.  Data Uncertainty in Face Recognition , 2014, IEEE Transactions on Cybernetics.

[12]  Jian Yang,et al.  Integrating Conventional and Inverse Representation for Face Recognition , 2014, IEEE Transactions on Cybernetics.

[13]  Shuyuan Yang,et al.  Unsupervised images segmentation via incremental dictionary learning based sparse representation , 2014, Inf. Sci..

[14]  David Zhang,et al.  Using the idea of the sparse representation to perform coarse-to-fine face recognition , 2013, Inf. Sci..

[15]  Kirk L. Kroeker,et al.  Face recognition breakthrough , 2009, Commun. ACM.

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

[17]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[18]  Licheng Jiao,et al.  Fast Multifeature Joint Sparse Representation for Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[19]  Jian Yang,et al.  A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition , 2011, IEEE Transactions on Circuits and Systems for Video Technology.