Cost-sensitive dual-bidirectional linear discriminant analysis

Abstract In most previous cost-sensitive feature extraction methods, the image matrix needs to be converted into vectors. The conversion always leads to a high computation complexity and small sample size problem. To address these issues, we propose a matrix-feature extraction method for face recognition, Cost-sensitive Dual-Bidirectional Linear Discriminant Analysis (CB2LDA). It is based on 2D image matrices, which greatly reduces the computation complexity and the probability of falling into small sample size problems. The proposed methods extract 2D matrix features from a diagonal block matrix containing both image matrix A and its transposition AT. With the block matrix, the scatter information in both directions is simultaneously considered in the projections, which helps to preserve the underlying data structure in images. Moreover, it aims to preserve the best cost-weighted discriminative information in the facial images, such that the misclassification costs reach a lower level. The experimental results validate the effectiveness and efficiency of the proposed method.

[1]  Shiguang Shan,et al.  Multi-View Discriminant Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[3]  Björn E. Ottersten,et al.  Example-dependent cost-sensitive decision trees , 2015, Expert Syst. Appl..

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  Lin Sun,et al.  Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification , 2019, Inf. Sci..

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

[7]  Haixia Wang,et al.  Bayesian multi-distribution-based discriminative feature extraction for 3D face recognition , 2015, Inf. Sci..

[8]  Huawen Liu,et al.  Fisher discrimination based low rank matrix recovery for face recognition , 2014, Pattern Recognit..

[9]  William Zhu,et al.  Attribute reduction of data with error ranges and test costs , 2012, Inf. Sci..

[10]  Bin Luo,et al.  2D-LPP: A two-dimensional extension of locality preserving projections , 2007, Neurocomputing.

[11]  Yuan-Hai Shao,et al.  Robust L1-norm two-dimensional linear discriminant analysis , 2015, Neural Networks.

[12]  Zhi-Hua Zhou,et al.  Cost-Sensitive Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Bing Huang,et al.  Cost-sensitive sequential three-way decision modeling using a deep neural network , 2017, Int. J. Approx. Reason..

[14]  Gene H. Golub,et al.  Matrix computations , 1983 .

[15]  Sang-Woong Lee,et al.  Robust face recognition via hierarchical collaborative representation , 2018, Inf. Sci..

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

[17]  Jian Yang,et al.  LPP solution schemes for use with face recognition , 2010, Pattern Recognit..

[18]  Jing Chai,et al.  Multiple-instance discriminant analysis , 2014, Pattern Recognit..

[19]  Nuno Vasconcelos,et al.  Cost-Sensitive Boosting , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[21]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Bin Gu,et al.  Chunk incremental learning for cost-sensitive hinge loss support vector machine , 2018, Pattern Recognit..

[23]  Hui Gao,et al.  Why direct LDA is not equivalent to LDA , 2006, Pattern Recognit..

[24]  Mohammed Bennamoun,et al.  Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[25]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[27]  Yuhua Qian,et al.  Test-cost-sensitive attribute reduction , 2011, Inf. Sci..

[28]  Dewen Hu,et al.  Comment on: "Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition" , 2008, Pattern Recognit..

[29]  Tommy W. S. Chow,et al.  A two-dimensional Neighborhood Preserving Projection for appearance-based face recognition , 2012, Pattern Recognit..

[30]  Bing Huang,et al.  Sequential three-way decision based on multi-granular autoencoder features , 2020, Inf. Sci..

[31]  Ming Li,et al.  Feature extraction using two-dimensional maximum embedding difference , 2014, Inf. Sci..

[32]  Jiwen Lu,et al.  Cost-sensitive subspace learning for face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[33]  Sang-Woong Lee,et al.  Classification of breast cancer histology images using incremental boosting convolution networks , 2019, Inf. Sci..

[34]  Kuldip K. Paliwal,et al.  Linear discriminant analysis for the small sample size problem: an overview , 2014, International Journal of Machine Learning and Cybernetics.

[35]  Lunke Fei,et al.  Robust Sparse Linear Discriminant Analysis , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[36]  Jiwen Lu,et al.  Cost-Sensitive Subspace Analysis and Extensions for Face Recognition , 2013, IEEE Transactions on Information Forensics and Security.

[37]  Zilan Hu,et al.  Comment on: "Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition" , 2008, Pattern Recognit..

[38]  Jiwen Lu,et al.  Cost-Sensitive Semi-Supervised Discriminant Analysis for Face Recognition , 2012, IEEE Transactions on Information Forensics and Security.

[39]  Dao-Qing Dai,et al.  Two-Dimensional Maximum Margin Feature Extraction for Face Recognition , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[40]  Jian Yang,et al.  Two-dimensional discriminant transform for face recognition , 2005, Pattern Recognit..

[41]  Haiping Lu,et al.  A survey of multilinear subspace learning for tensor data , 2011, Pattern Recognit..

[42]  Simon C. K. Shiu,et al.  Two-dimensional Laplacianfaces method for face recognition , 2008, Pattern Recognit..

[43]  Zhi-Hua Zhou,et al.  ON MULTI‐CLASS COST‐SENSITIVE LEARNING , 2006, Comput. Intell..

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

[45]  Jingjing Liu,et al.  Enhanced fisher discriminant criterion for image recognition , 2012, Pattern Recognit..

[46]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[47]  Bing Huang,et al.  Sequential three-way decision and granulation for cost-sensitive face recognition , 2016, Knowl. Based Syst..

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

[49]  Yong Wang,et al.  Feature extraction using a fast null space based linear discriminant analysis algorithm , 2012, Inf. Sci..

[50]  Xuelong Li,et al.  L1-Norm-Based 2DPCA , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[51]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[52]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).