Multiple-instance discriminant analysis

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• 作者：Jing Chai^a ; ^{jingchai@aliyun.com" class="auth_mail}Author Vitae ; Xinghao Ding^bAuthor Vitae ; Hongtao Chen^aAuthor Vitae ; Tingyu Li^aAuthor Vitae
• 关键词：Multiple-instance learning ; Feature extraction ; Dimensionality reduction ; Block coordinate ascent
• 刊名：Pattern Recognition
• 出版年：July, 2014
• 年：2014
• 卷：47
• 期：7
• 页码：2517-2531
• 全文大小：590 K

文摘

Multiple-instance discriminant analysis (MIDA) is proposed to cope with the feature extraction problem in multiple-instance learning. Similar to MidLABS, MIDA is also derived from linear discriminant analysis (LDA), and both algorithms can be treated as multiple-instance extensions of LDA. Different from MidLABS which learns from the bag level, MIDA is designed from the instance level. MIDA consists of two versions, i.e., binary-class MIDA (B-MIDA) and multi-class MIDA (M-MIDA), which are utilized to cope with binary-class (standard) and multi-class multiple-instance learning tasks, respectively. The block coordinate ascent approach, by which we seek positive prototypes (the most positive instance in a positive bag is termed as the positive prototype of this bag) and projection vectors alternatively and iteratively, is proposed to optimize B-MIDA and M-MIDA to obtain lower dimensional transformation subspaces. Extensive experiments empirically demonstrate the effectiveness of B-MIDA and M-MIDA in extracting discriminative components and weakening class-label ambiguities for instances in positive bags.