Ensemble multi-instance multi-label learning approach for video annotation task
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multi-instance methods for partially supervised image segmentation
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised multi-instance multi-label learning for video annotation task
Proceedings of the 20th ACM international conference on Multimedia
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we address the problem of multi-instance multi-label learning (MIML) where each example is associated with not only multiple instances but also multiple class labels. In our novel approach, given an MIML example, each instance in the example is only associated with a single label and the label set of the example is the aggregation of all instance labels. Many real-world tasks such as scene classification, text categorization and gene sequence encoding can be properly formalized under our proposed approach. We formulate our MIML problem as a combination of two optimizations: (1) a quadratic programming (QP) that minimizes the empirical risk with L2-norm regularization, and (2) an integer programing (IP) assigning each instance to a single label. We also present an efficient method combining the stochastic gradient decent and alternating optimization approaches to solve our QP and IP optimizations. In our experiments with both an artificially generated data set and real-world applications, i.e. scene classification and text categorization, our proposed method achieves superior performance over existing state-of-the-art MIML methods such as MIMLBOOST, MIMLSVM, M$^3$MIML and MIMLRBF.