Partial Multi-Label Learning With Noisy Label Identification

Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems with the disambiguation strategy, which recovers ground-truth labels from the candidate label set by simply assuming that the noisy labels are generated randomly. In real applications, however, noisy labels are usually caused by some ambiguous contents of the example. Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. The two objectives are formalized in a unified framework with trace norm and l1 norm regularizers. Under the supervision of the observed noise-corrupted label matrix, the multi-label classifier and noisy label identifier are jointly optimized by incorporating the label correlation exploitation and feature-induced noise model. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach.

[1]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[2]  Xu-Ying Liu,et al.  Partial Label Learning via Feature-Aware Disambiguation , 2016, KDD.

[3]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Min-Ling Zhang,et al.  Partial Multi-Label Learning via Credible Label Elicitation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yang Yu,et al.  Multi-label hypothesis reuse , 2012, KDD.

[8]  Rong Jin,et al.  Learning with Multiple Labels , 2002, NIPS.

[9]  Stefan Kramer,et al.  Online multi-label dependency topic models for text classification , 2018, Machine Learning.

[10]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[11]  Xu Sun,et al.  Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification , 2018, EMNLP.

[12]  Ben Taskar,et al.  Learning from Partial Labels , 2011, J. Mach. Learn. Res..

[13]  Zhi-Hua Zhou,et al.  Multi-Label Learning by Exploiting Label Correlations Locally , 2012, AAAI.

[14]  Yoshua Bengio,et al.  Learning from Partial Labels with Minimum Entropy , 2004 .

[15]  Thomas G. Dietterich,et al.  A Conditional Multinomial Mixture Model for Superset Label Learning , 2012, NIPS.

[16]  Yale Song,et al.  Improving Pairwise Ranking for Multi-label Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Hsuan-Tien Lin,et al.  Feature-aware Label Space Dimension Reduction for Multi-label Classification , 2012, NIPS.

[19]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[20]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Weiwei Liu,et al.  Discriminative and Correlative Partial Multi-Label Learning , 2019, IJCAI.

[22]  Tao Wang,et al.  Partial Multi-Label Learning by Low-Rank and Sparse Decomposition , 2019, AAAI.

[23]  Min-Ling Zhang,et al.  Feature-Induced Labeling Information Enrichment for Multi-Label Learning , 2018, AAAI.

[24]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[25]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[26]  Gang Niu,et al.  Active Feature Acquisition with Supervised Matrix Completion , 2018, KDD.

[27]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.

[28]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..

[29]  Fei Yu,et al.  Maximum margin partial label learning , 2017, Machine Learning.

[30]  Sheng-Jun Huang,et al.  Partial Multi-Label Learning , 2018, AAAI.

[31]  Yi Ma,et al.  The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.

[32]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[33]  Fei Yu,et al.  Solving the Partial Label Learning Problem: An Instance-Based Approach , 2015, IJCAI.

[34]  Bo An,et al.  Partial Label Learning with Self-Guided Retraining , 2019, AAAI.

[35]  Zhi-Hua Zhou,et al.  Multi-Label Learning with Global and Local Label Correlation , 2017, IEEE Transactions on Knowledge and Data Engineering.

[36]  Stephen P. Boyd,et al.  Rank minimization and applications in system theory , 2004, Proceedings of the 2004 American Control Conference.

[37]  Eyke Hüllermeier,et al.  Learning from ambiguously labeled examples , 2005, Intell. Data Anal..