Abstract：When the specific features of labels are extracted by most of the existing label-specific features learning methods, only the correlations among labels are taken into account and the correlations among instances and among features are neglected. Therefore, the classification accuracy are reduced. To solve this problem, an algorithm for multi-label label-specific features learning combined with multi-category correlation information is designed in this paper. Label correlation, feature correlation and instance correlation are considered. The label correlation between labels are calculated by cosine similarity. The similarity graph matrix is constructed to learn feature correlation and instance correlation. The specific features of labels are selected by the proposed algorithm compactly, the classification accuracy is improved and the problem of excessive dimensionality in multi-label classification is effectively solved.
[1] ZHANG J J, WU Q, SHEN C H, et al. Multilabel Image Classification with Regional Latent Semantic Dependencies. IEEE Transactions on Multimedia, 2018, 20(10): 2801-2813. [2] JIANG S Y, XU Y H, WANG T Y, et al. Multi-label Metric Transfer Learning Jointly Considering Instance Space and Label Space Distribution Divergence. IEEE Access, 2019, 7: 10362-10373. [3] LIU Y, WEN K W, GAO Q X, et al. SVM Based Multi-label Learning with Missing Labels for Image Annotation. Pattern Recognition, 2018, 78: 307-317. [4] ELISSEEFF A, WESTON J. A Kernel Method for Multi-labelled Classification // Proc of the 14th International Conference on Neural Information Processing Systems(Natural and Synthetic). New York, USA: ACM, 2001: 681-687. [5] QI G J, HUA X S, RUI Y, et al. Correlative Multi-label Video Annotation // Proc of the 16th ACM International Conference on Multimedia. New York, USA: ACM, 2007: 17-26. [6] LEWIS D D, YANG Y M, ROSE T G, et al. RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 2004, 5: 361-397. [7] ZHANG M L, ZHANG K. Multi-label Learning by Exploiting Label Dependency // Proc of the 16th ACM SIGKOD International Confe-rence on Knowledge Discovery and Data Mining. New York, USA: ACM, 2010: 999-1008. [8] GUO Y H, GU S C. Multi-label Classification Using Conditional Dependency Networks // Proc of the 22nd International Joint Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2011: 1300-1305. [9] HUANG S J, ZHOU Z H. Multi-label Learning by Exploiting Label Correlations Locally // Proc of the 26th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2012: 949-955. [10] HUANG S J, YU Y, ZHOU Z H, et al. Multi-label Hypothesis Reuse // Proc of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2012: 525-533. [11] ZHANG M L, WU L. LIFT: Multi-label Learning with Label-Specific Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. [12] XU S P, YANG X B, YU H L, et al. Multi-label Learning with Label-Specific Feature Reduction. Knowledge-Based Systems, 2016, 104: 52-61. [13] ZHANG J J, FANG M, LI X. Multi-label Learning with Discriminative Features for Each Label. Neurocomputing, 2015, 154: 305-316. [14] HUANG J, LI G R, HUANG Q M, et al. Learning Label Specific Features for Multi-label Classification // Proc of the IEEE International Conference on Data Mining. Washington, USA: IEEE, 2015: 181-190. [15] HUANG J, LI G R, HUANG Q M, et al. Learning Label-Specific Features and Class-Dependent Labels for Multi-label Classification. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3309-3323. [16] NGUYEN T T, NGUYEN T T T, LUONG A V, et al. Multi-label Classification via Label Correlation and First Order Feature Dependance in a Data Stream. Pattern Recognition, 2019, 90: 35-51. [17] HUANG J, LI G R, HUANG Q M, et al. Joint Feature Selection and Classification for Multilabel Learning. IEEE Transactions on Cybernetics, 2018, 48(3): 876-889. [18] WENG W, LIN Y J, WU S X, et al. Multi-Label Learning Based on Label-Specific Features and Local Pairwise Label Correlation. Neurocomputing, 2018, 273: 385-394. [19] ZHANG J, LI C D, CAO D L, et al. Multi-label Learning with Label-Specific Features by Resolving Label Correlations. Knowledge Based Systems, 2018, 159: 148-157. [20] DE ABREU I B M, MANTOVANI R G, CERRI R, et al. Incorporating Instance Correlations in Multi-label Classification via Label-Space // Proc of the International Joint Conference on Neural Network. Washington, USA: IEEE, 2017: 581-588. [21] HAN H R, HUANG M X, ZHANG Y, et al. Multi-label Learning with Label Specific Features Using Correlation Information. IEEE Access, 2019, 7: 11474-11484. [22] TAN Q Y, YU G X, DOMENICONI C, et al. Multi-view Weak-Label Learning Based on Matrix Completion // Proc of the SIAM International Conference on Data Mining. Berlin, Germany: Springer, 2018: 450-458. [23] CHEN Z S, WU X, CHEN Q G, et al. Multi-view Partial Multi-label Learning with Graph-Based Disambiguation // Proc of the AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2020: 3553-3560. [24] LIN Z C, GANESH A, WRIGHT J, et al. Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix[C/OL]. [2020-05-21]. https://people.eecs.berkeley.edu/~yima/psfile/rpca_algorithms.pdf. [25] BOUTELL M R, LUO J B, SHEN X P, et al. Learning Multi-label Scene Classification. Pattern Recognition, 2004, 37(9): 1757-1771. [26] READ J, MARTINO L, LUENGO D, et al. Efficient Monte Carlo Methods for Multi-dimensional Learning with Classifier Chains. Pattern Recognition, 2014, 47(3): 1535-1546. [27] WANG Y B, ZHENG W J, CHENG Y S, et al. Joint Label Completion and Label-Specific Features for Multi-label Learning Algorithm. Soft Computing, 2020, 24: 6553-6569.
2020, Vol. 33 
