基于流形学习和稀疏约束的快速特征提取算法

doi:10.11896/j.issn.1002-137X.2016.08.056

摘要/Abstract

摘要： 针对稀疏保持投影算法在特征提取过程中无监督和L1范数优化的计算量较大的问题,提出一种基于流形学习和稀疏约束的快速特征提取算法。首先通过逐类PCA构造级联字典,并基于该字典通过最小二乘法快速学习稀疏保持结构;其次构造用于描述不同子流形距离的局部类间散度函数；然后整合所学习到的稀疏表示信息和局部类间散度信息以达到既考虑判别效率又保持稀疏表示结构的目的;所提算法最终转化为一个求解广义特征值问题。在公共人脸数据库(Yale,ORL和Extended Yale B)中的测试结果验证了该方法的可行性和有效性。

关键词: 特征提取,稀疏表示,主元分析,流形学习,人脸识别

Abstract: Aiming at the problems of being unsupervised and time-consuming of L1 norm optimization in the existing sparsity preserving projection,by integrating the sparse representation information with the manifold structure of the data,a novel algorithm for fast feature extraction,named sparsity preserving discriminative learning (SPDL),was proposed.SPDL first creates a concatenated dictionary by class-wise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least square method.Secondly,a local between-class separability function is defined to characterize the scatter of the samples in different sub-manifolds.Then SPDL integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function.Finally,the proposed method is transformed into a problem of solving the generalized eigenvalue.Extensive experimental results on several public face databases demonstrate the effectiveness of the proposed approach.

Key words: Feature extraction,Sparse representation,Principal component analysis,Manifold learning,Face recognition

任迎春,王志成,陈宇飞,赵卫东,彭磊. 基于流形学习和稀疏约束的快速特征提取算法[J]. 计算机科学, 2016, 43(8): 277-281. https://doi.org/10.11896/j.issn.1002-137X.2016.08.056

REN Ying-chun, WANG Zhi-cheng, CHEN Yu-fei, ZHAO Wei-dong and PENG Lei. Fast Feature Extraction Algorithm Based on Manifold Learning and Sparsity Constraints[J]. Computer Science, 2016, 43(8): 277-281. https://doi.org/10.11896/j.issn.1002-137X.2016.08.056

参考文献

[1] Kumar K B,Venkataraman D.Object Detection Using RobustImage Features [M]∥Artificial Intelligence and Evolutionary Algorithms in Engineering Systems.Springer India,2015:285-295
[2] Zhang Wen,Tang Xi-jin,Yoshida T.TESC:An approach toText classification using Semi-supervised Clustering [J].Knowledge-Based Systems,2015,75:152-160
[3] Zhao Xue-yi,Li Xi,Zhang Zhong-fei.Multimedia Retrieval via Deep Learning to Rank [J].IEEE Signal Processing Letters,2015,22(9):1487-1491
[4] Li Cheng-husan,Ho Hsin-hua,Kuo Bor-chen,et al.A Semi-supervised Feature Extraction based on Supervised and Fuzzy-based Linear Discriminant Analysis for Hyperspectral Image Classification[J].Applied Mathematics & Information Sciences,2015,9(1L):81-87
[5] Zhang De-hai,Ding Da,Li Jin,et al.PCA Based Extracting Feature Using Fast Fourier Transform for Facial Expression Recognition [M]∥Transactions on Engineering Technologies.SpringerNetherlands,2015:413-424
[6] Lai Yi-qiang.Rotation Moment Invariant Feature Extraction Te-chniques for Image Matching[J].Applied Mechanics and Materials,2014,721:775-778
[7] Wold S,Esbensen K,Geladi P.Principal component analysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1-3):37-52
[8] Altman E I,Marco G,Varetto F.Corporate distress diagnosis:Comparisons using linear discriminant analysis and neural networks [J].Journal of Banking and Finance,1994,18(3):505-529
[9] Tenenbaum J,Silva V,Langford J.A global geometric frame-work for nonlinear dimensionality reduction [J].Science,2000,290(5500):2319-2322
[10] Mikhail B,Partha N.Laplacian eigenmaps for dimensionality reduction and data representation [J].Neural Computation,2003,5(6):1373-1396
[11] Roweis Sam T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding [J].Science,2000,290(5500):2323-2326
[12] He Xiao-fei,Niyogi Partha.Locality preserving projections [C]∥Proceedings of the Advances in Neural Information Processing Systems (NIPS).2003,6:585-591
[13] He Xiao-fei,Cai Deng,Yan Shui-cheng,et al.Neighborhood preserving embedding [C]∥ Tenth IEEE International Conference on IEEE(ICCV 2005).2005,2:1208-1213
[14] Yan Shui-cheng,Xu Dong,Zhang Ben-yu,et al.Graph embedding:a general framework for dimensionality reduction [J].IEEE Trans.Pattern Anal.Mach.Intell,2007,9(1):40-51
[15] Shrivastava A,Patel V M,Chellappa R.Multiple kernel learning for sparse representation-based classification [J].IEEE Tran-sactions on Image Processing,2014,23(7):3013-3024
[16] Bai Zuo,Huang Guang-bin,Wang Dan-wei,et al.Sparse extreme learning machine for classification [J].IEEE Transactions on Cybernetics,2014,44(10):1858-1870
[17] Hui Kang-hua,Li Chun-li,Zhang Lei.Sparse neighbor representation for classification [J].Pattern Recognition Letters,2012,33(5):661-669
[18] Feng Zhi-zhao,Yang Meng,Zhang Lei,et al.Joint discriminative dimensionality reduction and dictionary learning for face recognition [J].Pattern Recognition,2013,46(8):2134-2143
[19] Yang Meng,Zhang Lei,Yang Jian,et al.Regularized robust co-ding for face recognition [J].IEEE Transactions on Image Processing,2013,2(5):1753-1766
[20] Shao Ming,Ma Ming-bo,Fu Yun.Sparse manifold subspacelearning [M]∥Low-Rank and Sparse Modeling for Visual Analysis.Springer,2014:117-132
[21] Zhang Sheng-ping,Yao Hong-xun,Sun Xin,et al.Sparse coding based visual tracking:review and experimental comparison [J].Pattern Recognition,2013,46(7):1772-1788
[22] Zhang Sheng-ping,Yao Hong-xun,Zhou Hui-yu,et al.Robust visual tracking based on online learning sparse representation [J].Neurocomputing,2013,100(1):31-40
[23] Wright J,Yang A,Sastry S,et al.Robust face recognition via sparse representation [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227
[24] Guan Nai-yang,Tao Da-cheng,Luo Zhi-gang,et al.Mahnmf:Manhattan Non-negative Matrix Factorization[J].Journal of Machine Learning Research,2012,arXiv:1207.3438
[25] Qiao Li-shan,Chen Song-can,Tan Xiao-yang.Sparsity preserving projections with applications to face recognition[J].Pattern Recognition,2010,43(1):331-341
[26] Qiao Li-shan,Chen Song-can,Tan Xiao-yang.Sparsity preserving discriminant analysis for single training image face recognition [J].Pattern Recognition Letters,2010,1(5):422-429
[27] Zang Fei,Zhang Jiang-she.Discriminative learning by sparserepresentation for classification [J].Neurocomputing,2011,4(12):2176-2183
[28] Gui Jie,Sun Zhe-nan,Jia Wei,et al.Discriminant sparse neighborhood preserving embedding for face recognition [J].Pattern Recognition,2012,5 (8):2884-2893
[29] Lou Song-jiang,Zhang Guo-yin,Pan Hai-wei.Supervised lapupacian discriminant analysis for small sample size problem with its application to face recognition [J].Journal of Computer Research and Development,2012,49(8):1730-1737
[30] Belhumeur P N,Hespanha J,Kriegman D.Eigenfaces vs.Fisherfaces:recognition using class specific linear projection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720
[31] Samaria F,Harter A.Parameterisation of a stochastic model for human face identification[C]∥Second IEEE Workshop on Applications of Computer Vision.Sarasota,1994:138-142
[32] Lee K,Ho J,Kriegman D.Acquiring linear subspaces for face recognition under variable lighting [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):684-698

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