Compositional metric learning for multi-label classification
Yan-Ping SUN1,2, Min-Ling ZHANG1,2,3()
1. School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3. Collaborative Innovation Center forWireless Communications Technology, Nanjing 211100, China
Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.
M L Zhang, Z H Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819–1837 https://doi.org/10.1109/TKDE.2013.39
2
E Gibaja, S Ventura. A tutorial on multilabel learning. ACM Computing Surveys, 2015, 47(3): 52 https://doi.org/10.1145/2716262
3
F Briggs, B Lakshminarayanan, L Neal, X Z Fern, R Raich, S J Hadley, A S Hadley, M G Betts. Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. Journal of the Acoustical Society of America, 2012, 131(6): 4640–4650 https://doi.org/10.1121/1.4707424
4
R Cabral, F DelaTorre, J P Costeira, A Bernardino. Matrix completion for weakly-supervised multi-label image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 121–135 https://doi.org/10.1109/TPAMI.2014.2343234
5
J Liu, W C Chang, Y Wu, Y Yang. Deep learning for extreme multi-label text classification. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 115–124 https://doi.org/10.1145/3077136.3080834
6
X Pan, Y X Fan, J Jia, H B Shen. Identifying RNA-binding proteins using multi-label deep learning. Science China Information Sciences, 2019, 62: 19103 https://doi.org/10.1007/s11432-018-9558-2
7
L Sun, H Ge, W Kang. Non-negative matrix factorization based modeling and training algorithm for multi-label learning. Frontiers of Computer Science, 2019, 13(6): 1243–1254 https://doi.org/10.1007/s11704-018-7452-y
F Wang, J Sun. Survey on distance metric learning and dimensionality reduction in data mining. Data Mining and Knowledge Discovery, 2015, 29(2): 534–564 https://doi.org/10.1007/s10618-014-0356-z
10
W Liu, I W Tsang. Large margin metric learning for multi-label prediction. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2800–2806
11
H Goukand, B Pfahringer, M Cree. Learning distance metrics for multilabel classification. In: Proceedings of the 8th Asian Conference on Machine Learning. 2016, 318–333
12
Y Zhang, J Schneider. Maximum margin output coding. In: Proceedings of the 29th International Conference on Machine Learning. 2012, 1575–1582
13
Y Verma, C V Jawahar. Image annotation by propagating labels from semantic neighbourhoods. International Journal of Computer Vision, 2017, 121(1): 126–148 https://doi.org/10.1007/s11263-016-0927-0
14
H Gouk, B Pfahringer, M Cree. Learning similarity metrics by factorising adjacency matrices. 2015, arXiv preprint arXiv: 1511.06442
15
J Ni, J Liu, C Zhang, D Ye, Z Ma. Fine-grained patient similarity measuring using deep metric learning. In: Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 2017, 1189–1198 https://doi.org/10.1145/3132847.3133022
16
Y Shi, A Bellet, F Sha. Sparse compositional metric learning. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 2078–2084
17
J St. Amand, J Huan. Sparse compositional local metric learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017, 1097–1104 https://doi.org/10.1145/3097983.3098153
ML Zhang, L Wu. LIFT:multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107–120 https://doi.org/10.1109/TPAMI.2014.2339815
20
J Huang, G Li, Q Huang, X Wu. Learning label-specific features and class-dependent labels for multi-label classification. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3309–3323 https://doi.org/10.1109/TKDE.2016.2608339
21
K Q Weinberger, L K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 2009, 10: 207–244
22
S J Huang, Z H Zhou. Multi-label learning by exploiting label correlations locally. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 2012, 949–955
23
Y Zhu, J Kwok, Z H Zhou. Multi-label learning with global and local correlation. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(6): 1081–1094 https://doi.org/10.1109/TKDE.2017.2785795
24
G X Yuan, C H Ho, C J Lin. An improved GLMNET for L1-regularized logistic regression. Journal of Machine Learning Research, 2012, 13: 1999–2030 https://doi.org/10.1145/2020408.2020421
25
A Beck, M Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Siam Journal on Imaging Sciences, 2009, 2(1): 183–202 https://doi.org/10.1137/080716542
26
K C Toh, S Yun. An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems. Pacific Journal of Optimization, 2010, 6(3): 615–640
J Read, B Pfahringer, G Holmes, E Frank. Classifier chains for multi-label classification. Machine Learning, 2011, 85(3): 333–359 https://doi.org/10.1007/s10994-011-5256-5
J Rong, S Wang, Z H Zhou. Learning a distance metric from multiinstance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2009, 896–902 https://doi.org/10.1109/CVPRW.2009.5206684
31
Y Verma, C V Jawahar. A robust distance with correlated metric learning for multi-instance multi-label data. In: Proceedings of the 24th ACM International Conference on Multimedia. 2016, 441–445 https://doi.org/10.1145/2964284.2967259
32
M L Zhang, Y K Li, Y Y Liu, X Geng. Binary relevance for multi-label learning: an overview. Frontiers of Computer Science, 2018, 12(2): 191–202 https://doi.org/10.1007/s11704-017-7031-7
33
Y Wu, Y Lin, X Dong, Y Yan, W Bian, Y Yang. Progressive learning for person re-identification with one example. IEEE Transactions on Image Processing, 2019, 28(6): 2872–2881 https://doi.org/10.1109/TIP.2019.2891895
34
L Sun, S Ji, J Ye. Multi-label Dimensionality Reduction. London: Chapman and Hall/CRC, 2013
35
R B Pereira, A Plastino, B Zadrozny, L H C Merschmann. Categorizing feature selection methods for multi-label classification. Artificial Intelligence Review, 2018, 49(1): 57–78 https://doi.org/10.1007/s10462-016-9516-4
36
J Zhang, C Li, D Cao, Y Lin, S Su, L Dai, S Li. Multi-label learning with label-specific features by resolving label correlations. Knowledge-Based Systems, 2018, 159: 148–157 https://doi.org/10.1016/j.knosys.2018.07.003
37
Z S Chen, M L Zhang. Multi-label learning with regularization enriched label-specific features. In: Proceedings of the 11th Asian Conference on Machine Learning. 2019, 411–424
38
Y Yang, S Gopal. Multilabel classification with meta-level features in a learning-to-rank framework. Machine Learning, 2012, 88(1–2): 47–68 https://doi.org/10.1007/s10994-011-5270-7
39
S Canuto, M A Gonçalves, F Benevenuto. Exploiting new sentimentbased meta-level features for effective sentiment analysis. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 53–62 https://doi.org/10.1145/2835776.2835821
40
X Zhu, X Li, S Zhang. Block-row sparse multiview multilabel learning for image classification. IEEE Transactions on Cybernetics, 2016, 46(2): 450–461 https://doi.org/10.1109/TCYB.2015.2403356
41
C Zhang, Z Yu, Q Hu, P Zhu, X Liu, X Wang. Latent semantic aware multi-view multi-label classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 4414–4421
42
X Wu, Q G Chen, Y Hu, D B Wang, X Chang, X Wang, M L Zhang. Multiview multi-label learning with view-specific information extraction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 3884–3890 https://doi.org/10.24963/ijcai.2019/539
Z H Zhou. Abductive learning: towards bridging machine learning and logical reasoning. Science China Information Sciences, 2019, 62: 076101 https://doi.org/10.1007/s11432-018-9801-4
45
Y Yang, Z Ma, A G Hauptmann, N Sebe. Feature selection for multimedia analysis by sharing information among multiple tasks. IEEE Transactions on Multimedia, 2013, 15(3): 661–669 https://doi.org/10.1109/TMM.2012.2237023
46
R Zhang, F Nie, X Li. Self-weighted supervised discriminative feature selection. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3913–3918 https://doi.org/10.1109/TNNLS.2017.2740341
47
R Zhang, F Nie, Y Wang, X Li. Unsupervised feature selection via adaptive multimeasure fusion. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(9): 2886–2892 https://doi.org/10.1109/TNNLS.2018.2884487