[1]ZHA Siming,BAO Qingsen,LUO Jian,et al.Adaptive label correlation and instance correlation guided incomplete multiview weak label learning[J].CAAI Transactions on Intelligent Systems,2022,17(4):670-679.[doi:10.11992/tis.202106017]
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Adaptive label correlation and instance correlation guided incomplete multiview weak label learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 670-679 Column: 学术论文—机器学习 Public date: 2022-07-05
Title:
Adaptive label correlation and instance correlation guided incomplete multiview weak label learning
Author(s):
ZHA Siming1;  BAO Qingsen1;  LUO Jian1; 2;  CHEN Lei1; 2
1. School of Computer Science, Nanjing University of Post and Telecommunication, Nanjing 210003, China;
2. Jiangsu Key Laboratory of Big data Security & Intelligent Processing, Nanjing University of Post and Telecommunications, Nanjing 210003, China
Keywords:
multi-view learning; multi-label learning; graph learning; manifold regularization; weakly supervised learning; matrix factorization; missing labels; missing views
CLC:
TP181
DOI:
10.11992/tis.202106017
Abstract:
Focusing on the problem of incomplete view and label in multiview multilabel learning, the paper proposes a model called adaptive label correlation and instance correlation guided incomplete multiview weak label learning. The model assumes each view of the instance is obtained from a common representation through different mappings. Firstly, matrix factorization is used by embedding the indicator matrix to make full use of the existing incomplete multiview weak label data and then introduces the technology of learning the standard Laplacian matrix in graph theory to describe the label correlation and instance correlation. Therefore, embedding manifold regularization in the model to make the learned common representation and classifier more reasonable. Finally, four multiview multilabel datasets were conducted in a series of experiments. The experimental results show that the proposed model can solve the task of incomplete multiview weak label learning effectively.
References:
[1] ZHOU Tianyi, TAO Dacheng, WU Xindong. Compressed labeling on distilled labelsets for multi-label learning[J]. Machine learning, 2012, 88(1/2): 69–126.
[2] XU Linli, WANG Zhen, SHEN Zefan, et al. Learning low-rank label correlations for multi-label classification with missing labels[C]//2014 IEEE International Conference on Data Mining. Shenzhen: IEEE, 2014: 1067?1072.
[3] ZHU Yue, KWOK J T, ZHOU Zhihua. Multi-label learning with global and local label correlation[J]. IEEE transactions on knowledge and data engineering, 2018, 30(6): 1081–1094.
[4] ZHANG Minling, ZHOU Zhihua. A review on multi-label learning algorithms[J]. IEEE transactions on knowledge and data engineering, 2014, 26(8): 1819–1837.
[5] BOUTELL M R, LUO Jiebo, SHEN Xipeng, et al. Learning multi-label scene classification[J]. Pattern recognition, 2004, 37(9): 1757–1771.
[6] FüRNKRANZ J, HüLLERMEIER E, LOZA MENCíA E, et al. Multilabel classification via calibrated label ranking[J]. Machine learning, 2008, 73(2): 133–153.
[7] TSOUMAKAS G, VLAHAVAS I. Random k-labelsets: an ensemble method for multilabel classification[M]//Machine Learning: ECML 2007. Heidelberg: Springer Berlin Heidelberg, 2007: 406?417.
[8] ZHANG Jia, LUO Zhiming, LI Candong, et al. Manifold regularized discriminative feature selection for multi-label learning[J]. Pattern recognition, 2019, 95: 136–150.
[9] ZHANG Changqing, YU Ziwei, FU Huazhu, et al. Hybrid noise-oriented multilabel learning[J]. IEEE transactions on cybernetics, 2020, 50(6): 2837–2850.
[10] SUN Yuyin, ZHANG Yin, ZHI Zhihua. Multi-label learning with weak label[C]//Proceedings of the 24th AAAI Conference on Artificial Intelligence. Georgia: AAAI Press, 2010: 593?598.
[11] LI Junbing, ZHANG Changqing, ZHU Pengfei, et al. SPL-MLL: selecting predictable landmarks for multi-label learning[C]//European Conference on Computer Vision. Cham: Springer, 2020: 783?799.
[12] ZHANG Minling, ZHOU Zhihua. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern recognition, 2007, 40(7): 2038–2048.
[13] CLARE A, KING R D. Knowledge discovery in multi-label phenotype data[M]//Principles of Data Mining and Knowledge Discovery. Heidelberg: Springer Berlin Heidelberg, 2001: 42?53.
[14] GHAMRAWI N, MCCALLUM A. Collective multi-label classification[C]//CIKM ’05: Proceedings of the 14th ACM international conference on Information and knowledge management. New York: ACM, 2005: 195?200.
[15] ZHANG Changqing, YU Ziwei, HU Qinghua, et al. Latent semantic aware multi-view multi-label Classification[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Louisiana: AAAI Press, 2018: 2703?2709.
[16] LIU Meng, LUO Yong, TAO Dacheng, et al. Low-rank multi-view learning in matrix completion for multi-label image classification[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Texas: AAAI Press, 2015: 2278?2284.
[17] ZHANG Yongshan, WU Jia, CAI Zhihua, et al. Multi-view multi-label learning with sparse feature selection for image annotation[J]. IEEE transactions on multimedia, 2020, 22(11): 2844–2857.
[18] TAN Qiaoyu, YU Guoxian, DOMENICONI C, et al. Incomplete multi-view weak-label learning[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden. California: International Joint Conferences on Artificial Intelligence Organization, 2018: 2703?2709.
[19] ZHANG Changqing, CUI Yajie, HAN Zongbo, et al. Deep partial multi-view learning[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(5): 2402–2415.
[20] 张祎, 孔祥维, 王振帆, 等. 基于多视图矩阵分解的聚类分析[J]. 自动化学报, 2018, 44(12): 2160–2169
ZHANG Yi, KONG Xiangwei, WANG Zhenfan, et al. Matrix factorization for multi-view clustering[J]. Acta automatica sinica, 2018, 44(12): 2160–2169
[21] 孙亮, 韩毓璇, 康文婧, 等. 基于生成对抗网络的多视图学习与重构算法[J]. 自动化学报, 2018, 44(5): 819–828
SUN Liang, HAN Yuxuan, KANG Wenjing, et al. Multi-view learning and reconstruction algorithm via generative adversarial networks[J]. Acta automatica sinica, 2018, 44(5): 819–828
[22] XU Chang, TAO Dacheng, XU Chao. Multi-view learning with incomplete views[J]. IEEE transactions on image processing:a publication of the IEEE signal processing society, 2015, 24(12): 5812–5825.
[23] DATTA R, JOSHI D, LI Jia, et al. Image retrieval: ideas, influences, and trends of the new age[J]. ACM computing surveys, 2008, 40(2): 5.
[24] DONG Haochen, LI Yufeng, ZHOU Zhihua. Learning from semi-supervised weak-label data[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Stockholm: International Joint Conferences on Artificial Intelligence Organization, 2018, 32(1).
[25] LUO Dijun, DING C, HUANG Heng, et al. Non-negative Laplacian embedding[C]//2009 Ninth IEEE International Conference on Data Mining. Miami Beach: IEEE, 2009: 337?346.
[26] WANG Hua, HUANG Heng, DING C. Image annotation using multi-label correlated Green’s function[C]//2009 IEEE 12th International Conference on Computer Vision. Kyoto: IEEE, 2009: 2029?2034.
[27] CHUNG F R K, GRAHAM F C. Spectral graph theory [M]//Rhode Island: American Mathematic Society, 1997.
[28] 杨明, 刘先忠. 矩阵论[M]. 武汉: 华中工学院出版社, 2003.
[29] BOUMAL N, MISHRA B, ABSIL P A, et al. Manopt, a Matlab toolbox for optimization on manifolds[J]. Journal of machine learning research, 2014, 15: 1455–1459.
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