[1]XU Ziwei,CHEN Xiuhong.Sparse optimal mean principal component analysis based on self-paced learning[J].CAAI Transactions on Intelligent Systems,2021,16(3):416-424.[doi:10.11992/tis.201911028]
Copy
Sparse optimal mean principal component analysis based on self-paced learning
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 3
Page number:
416-424
Column:
学术论文—机器学习
Public date:
2021-05-05
- Title:
- Sparse optimal mean principal component analysis based on self-paced learning
- Keywords:
- image processing; principal component analysis; unsupervised learning; data dimension deduction; sparse; optimal mean; self-paced learning; face recognition
- CLC:
- TP391.4
- DOI:
- 10.11992/tis.201911028
- Abstract:
- Principal component analysis (PCA) can be referred to as an unsupervised dimensionality reduction approach. However, the existing methods do not consider the difference of samples and cannot jointly extract important information of samples, thus affecting the performance of some methods. For the above problems, based on self-paced learning, we proposed a sparse optimal mean PCA algorithm. In our model, loss of function is defined by $ {L_{{\rm{2,1}}}}$ norm, the projection matrix is regularized by $ {L_{{\rm{2,1}}}}$ norm, and the mean value is taken as a variable to be optimized in the iteration. In this way, important features can be consistently selected, and the robustness of the method to outliers can be improved. Considering the difference in training samples, we utilized self-paced learning mechanism to complete the learning process of training samples from “simple” to “complex” so as to effectively reduce the influence of outliers. Theoretical analysis and the empirical study revealed that the proposed method could effectively reduce the influence of noise or outliers on the classification progress, thus improving the effect of the classification.
- References:
-
[1] OU Weihua, YOU Xinge, TAO Dacheng, et al. Robust face recognition via occlusion dictionary learning[J]. Pattern recognition, 2014, 47(4):1559-1572.
[2] WEI Lai, ZHOU Rigui, YIN Jun, et al. Latent graph-regularized inductive robust principal component analysis[J]. Knowledge-based systems, 2019, 177:68-81.
[3] HE Jinrong, BI Yingzhou, LIU Bin, et al. Graph-dual Laplacian principal component analysis[J]. Journal of ambient intelligence and humanized computing, 2019, 10(8):3249-3262.
[4] ZHAO Haifeng, WANG Zheng, NIE Feiping. A new formulation of linear discriminant analysis for robust dimensionality reduction[J]. IEEE transactions on knowledge and data engineering, 2018, 31(4):629-640.
[5] 朱换荣, 郑智超, 孙怀江. 面向局部线性回归分类器的判别分析方法[J]. 智能系统学报, 2019, 14(5):959-965
ZHU Huanrong, ZHENG Zhichao, SUN Huaijiang. Locality-regularized linear regression classification-based discriminant analysis[J]. CAAI transactions on intelligent systems, 2019, 14(5):959-965
[6] NIE Feiping, HUANG Heng, DING C, et al. Robust principal component analysis with non-greedy l1-norm maximization[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Barcelona, Catalonia, Spain, 2011:1433-1438.
[7] NIE Feiping, YUAN Jianjun, HUANG Heng. Optimal mean robust principal component analysis[C]//Proceedings of the 31st International Conference on Machine Learning. Beijing, China, 2014:1062-1070.
[8] ZOU Hui, HASTIE T, TIBSHIRANI R. Sparse principal component analysis[J]. Journal of computational and graphical statistics, 2006, 15(2):265-286.
[9] YI Shuangyan, LAI Zhihui, HE Zhenyu, et al. Joint sparse principal component analysis[J]. Pattern recognition, 2017, 61:524-536.
[10] BENGIO Y, LOURADOUR J, COLLOBERT R, et al. Curriculum learning[C]//Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec, Canada, 2009:41-48.
[11] KUMAR M P, PACKER B, KOLLER D. Self-paced learning for latent variable models[C]//Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada, 2010:1189-1197.
[12] JIANG Lu, MENG Deyu, ZHAO Qian, et al. Self-paced curriculum learning[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, Texas, USA, 2015:2694-2700.
[13] YU Hao, WEN Guoqiu, GAN Jiangzhang, et al. Self-paced learning for K-means clustering algorithm[J]. Pattern recognition letters, 2020, 132:69-75.
[14] HU Sixiu, PENG Jiangtao, FU Yingxiong, et al. Kernel joint sparse representation based on self-paced learning for hyperspectral image classification[J]. Remote sensing, 2019, 11(9):1114-1132.
[15] HAJINEZHAD D, SHI Qingjiang. Alternating direction method of multipliers for a class of nonconvex bilinear optimization:convergence analysis and applications[J]. Journal of global optimization, 2018, 70(1):261-288.
[16] HOU Chenping, NIE Feiping, YI Dongyun, et al. Feature selection via joint embedding learning and sparse regression[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Barcelona, Spain, 2011:1324-1329.
[17] LYONS M J, BUDYNEK J, AKAMATSU S. Automatic classification of single facial images[J]. IEEE transactions on pattern analysis and machine intelligence, 1999, 21(12):1357-1362.
[18] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation.[J]. IEEE transactions on pattern analysis and machine intelligence, 2009, 31(2):210-227.
[19] JESORSKY O, KIRCHBERG K J, FRISCHHOLZ R W. Robust face detection using the hausdorff distance[C]//Proceedings of the 3rd International Conference on Audio- and Video-Based Biometric Person Authentication. Halmstad, Sweden, 2001:90-95.
[20] ZHENG Miao, BU Jiajun, CHEN C, et al. Graph regularized sparse coding for image representation[J]. IEEE transactions on image processing, 2011, 20(5):1327-1336.
[21] KUSSUL E, BAIDYK T. Improved method of handwritten digit recognition tested on MNIST database[J]. Image and vision computing, 2004, 22(12):971-981.
- Similar References:
Memo
-
Last Update:
2021-06-25