A multi-instance multi-label learning algorithm based on instance correlations

• Published in: Multimedia tools and applications Vol. 75; no. 19; pp. 12263 - 12284
• Main Authors: Liu, Chanjuan, Chen, Tongtong, Ding, Xinmiao, Zou, Hailin, Tong, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2016; Springer Nature B.V
• Subjects: Algorithms; Analysis; Artificial intelligence; Classification; Computer Communication Networks; Computer engineering; Computer Science; Correlation; Data Structures and Information Theory; Graphs; Image processing systems; Kernels; Learning; Machine learning; Multimedia; Multimedia Information Systems; Special Purpose and Application-Based Systems; State of the art; Studies; Support vector machines; Instance correlations; Multi-instance multi-label learning; Multi-label classification; Multiple-kernel fusion
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-016-3494-z

Existing multi-instance multi-label learning algorithms generally assume that instances in a bag are independent of each other, which is difficult to be guaranteed in practical applications. A novel multi-instance multi-label learning algorithm is proposed by modeling instance correlations in each bag. First, instance correlations are introduced in multi-instance multi-label learning by constructing graphs. Then, different kernel matrices are derived from kernel functions based on graphs at different scales, which are employed to train Multiple Kernel Support Vector Machine (MKSVM) classifiers. Experimental results on different datasets show that the proposed method significantly improves the accuracy of the multi-label classification compared with the state-of-the-art methods.