[1]XU Guangsheng,WANG Shitong.Incomplete multi-view subspace learning through dual low-rank decompositions[J].CAAI Transactions on Intelligent Systems,2022,17(6):1084-1092.[doi:10.11992/tis.202107002]
Copy

Incomplete multi-view subspace learning through dual low-rank decompositions

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1084-1092 Column: 学术论文—机器学习 Public date: 2022-11-05
Title:
Incomplete multi-view subspace learning through dual low-rank decompositions
Author(s):
XU Guangsheng1;  WANG Shitong2
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Media Design and Software Technology of Jiangsu Province, Jiangnan University, Wuxi 214122, China
Keywords:
subspace learning; supervised learning; incomplete multi-view; latent factors; low-rank constraint; dual low-rank decompositions; feature alignment; low-dimensional feature
CLC:
TP181
DOI:
10.11992/tis.202107002
Abstract:
Multi-view data are very common in real-world applications. Different viewpoints and sensors tend to facilitate better data representation. However, data from various perspectives show a significant variation. Especially when only incomplete multi-view data are available, the corresponding multi-view learning may result in poor performance or even training failure. This study proposes a multi-view learning algorithm called IMSL (Incomplete Multi-View Subspace Learning through Dual Low-Rank Decompositions) to tackle this issue. The proposed algorithm addresses the incomplete multi-view problem in two ways: (1) Latent factors are introduced into a dual low-rank decomposition subspace framework to mine missing information in the multi-view data. (2) IMSL seeks a more robust subspace through pre-learned low-dimensional features of multi-view data. Furthermore, the supervised data are used to guide dual low-rank decompositions. Experimental results show that the proposed algorithm outperforms the previous multi-view subspace learning algorithms on the adopted incomplete multi-view datasets. Pre-learning low-dimensional features of multi-view data, on the other hand, can improve robustness, and dual low-rank decomposition can be guided in a supervised manner.
References:
[1] XU Chang, TAO Dacheng, XU Chao. A survey on multi-view learning[EB/OL]. (2013-04-20)[2021-07-01]. https://arxiv.org/abs/1304.5634.
[2] 吴钟强, 张耀文, 商琳. 基于语义特征的多视图情感分类方法[J]. 智能系统学报, 2017, 12(5): 745–751
WU Zhongqiang, ZHANG Yaowen, SHANG Lin. Multi-view sentiment classification of microblogs based on semantic features[J]. CAAI transactions on intelligent systems, 2017, 12(5): 745–751
[3] ZHANG Changqing, HU Qinghua, FU Huazhu, et al. Latent multi-view subspace clustering[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017 : 4333-4341.
[4] BRBI? M, KOPRIVA I. Multi-view low-rank sparse subspace clustering[J]. Pattern recognition, 2018, 73: 247–258.
[5] WU Xinxiao, JIA Yunde. View-invariant action recognition using latent kernelized structural SVM[M]//Computer Vision-ECCV 2012. Berlin: Springer Berlin Heidelberg, 2012: 411-424.
[6] ZHU Pengfei, HUI Binyuan, ZHANG Changqing, et al. Multi-view deep subspace clustering networks[EB/OL]. (2019-08-06) [2021-07-01]. https://arxiv.org/abs/1908.01978.
[7] XIE Yuan, LIN Bingqian, QU Yanyun, et al. Joint deep multi-view learning for image clustering[J]. IEEE transactions on knowledge and data engineering, 2021, 33(11): 3594–3606.
[8] DING Zhengming, SUH S, HAN J J, et al. Discriminative low-rank metric learning for face recognition[C]//2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana: IEEE, 2015, 1: 1-6.
[9] SHAO Ming, KIT D, FU Yun. Generalized transfer subspace learning through low-rank constraint[J]. International journal of computer vision, 2014, 109(1/2): 74–93.
[10] LIU Guangcan, LIN Zhouchen, YAN Shuicheng, et al. Robust recovery of subspace structures by low-rank representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(1): 171–184.
[11] ZHANG Fanlong, YANG Jian, TAI Ying, et al. Double nuclear norm-based matrix decomposition for occluded image recovery and background modeling[J]. IEEE transactions on image processing, 2015, 24(6): 1956–1966.
[12] YU C N J, JOACHIMS T. Learning structural SVMs with latent variables[C]//ICML’09: Proceedings of the 26th Annual International Conference on Machine Learning. New York: ACM, 2009: 1169-1176.
[13] LI Jia, NAJMI A, GRAY R M. Image classification by a two-dimensional hidden Markov model[J]. IEEE transactions on signal processing, 2000, 48(2): 517–533.
[14] DING Zhengming, SHAO Ming, FU Yun. Missing modality transfer learning via latent low-rank constraint[J]. IEEE transactions on image processing, 2015, 24(11): 4322–4334.
[15] DING Zhengming, ZHAO Handong, FU Yun. Missing modality transfer learning[M]//Learning Representation for Multi-View Data Analysis. Cham: Springer International Publishing, 2018: 147-173.
[16] LIU Guangcan, YAN Shuicheng. Latent Low-Rank Representation for subspace segmentation and feature extraction[C]//2011 International Conference on Computer Vision. Barcelona: IEEE, 2011: 1615-1622.
[17] DING Zhengming, FU Yun. Robust Multi-view Subspace Learning through dual low-rank decompositions[C]//AAAI’16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. New York: ACM, 2016: 1181-1187.
[18] DING Zhengming, SHAO Ming, FU Yun. Incomplete multisource transfer learning[J]. IEEE transactions on neural networks and learning systems, 2018, 29(2): 310–323.
[19] 程旸, 王士同. 基于局部保留投影的多可选聚类发掘算法[J]. 智能系统学报, 2016, 11(5): 600–607
CHENG Yang, WANG Shitong. A multiple alternative clusterings mining algorithm using locality preserving projections[J]. CAAI transactions on intelligent systems, 2016, 11(5): 600–607
[20] LIU Guangcan, ZHOU chenlin, YONG Yu. Robust subspace segmentation by low-rank representation[C]// International Conference on Machine Learning. Haifa: DBLP, 2010: 663-670.
[21] RAZZAGHI P, RAZZAGHI P, ABBASI K. Transfer subspace learning via low-rank and discriminative reconstruction matrix[J]. Knowledge-based systems, 2019, 163: 174–185.
[22] SU Ya, LI Sheng, WANG Shengjin, et al. Submanifold decomposition[J]. IEEE transactions on circuits and systems for video technology, 2014, 24(11): 1885–1897.
[23] HE Xiaofei, NIYOGI P. Locality preserving projections[J]. Advances in neural information processing systems, 2003, 16(1): 186–197.
[24] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(7): 711–720.
[25] DING Zhengming, FU Yun. Low-rank common subspace for multi-view learning[C]//2014 IEEE International Conference on Data Mining. Shenzhen: IEEE, 2014 : 110-119.
[26] DING Zhengming, FU Yun. Robust multiview data analysis through collective low-rank subspace[J]. IEEE transactions on neural networks and learning systems, 2018, 29(5): 1986–1997.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems