Weighted Two-Phase Linear Reconstruction Measure-based Classification

Linear reconstruction measure (LRM) is a promising similarity measure of data. In this paper, we consider the locality of data in LRM, and propose weighted two-phase linear reconstruction measure-based classification (WTPLRMC). In WTPLRMC, the first phase determines the representative training samples from all training samples by LRM, and the second phase constrains the linear reconstruction coefficients of the chosen representative training samples in first phase using the locality of data, which is reflected by the similarity weights between each test sample and the representative training samples. The effectiveness of the proposed WTPLRMC is well demonstrated on some benchmark face databases with satisfactory classification results.

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

[2]  Luc Van Gool,et al.  Adaptive and Weighted Collaborative Representations for image classification , 2014, Pattern Recognit. Lett..

[3]  Jianping Gou,et al.  Sparse Coefficient-Based ${k}$ -Nearest Neighbor Classification , 2017, IEEE Access.

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

[5]  Long Wang,et al.  Integrating Globality and Locality for Robust Representation Based Classification , 2014 .

[6]  Jiang Li,et al.  A new decision rule for sparse representation based classification for face recognition , 2013, Neurocomputing.

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

[8]  Yongzhao Zhan,et al.  Two-phase linear reconstruction measure-based classification for face recognition , 2018, Inf. Sci..

[9]  Zhang Yi,et al.  Collaborative neighbor representation based classification using l2-minimization approach , 2013, Pattern Recognit. Lett..

[10]  Jianping Gou,et al.  Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification , 2017, Neural Computing and Applications.

[11]  Jian Yang,et al.  Linear reconstruction measure steered nearest neighbor classification framework , 2014, Pattern Recognit..

[12]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Qi Zhu,et al.  L1-norm plus L2-norm sparse parameter for image recognition , 2015 .

[14]  Jun Guo,et al.  Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Daoqiang Zhang,et al.  Combining L1-norm and L2-norm based sparse representations for face recognition , 2015 .

[16]  Ying-Ke Lei,et al.  Face recognition via Weighted Sparse Representation , 2013, J. Vis. Commun. Image Represent..