Multiple-instance discriminant analysis

Jing Chai

doi:10.1016/j.patcog.2014.02.002

Return

Multiple-instance discriminant analysis

2014-07-01

PRE

DOI：10.1016/j.patcog.2014.02.002

Original

Original request for help

Save

Abstract

En 中文

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. (C) 2014 Elsevier Ltd. All rights reserved.

Keywords:

Multiple-instance learning

Feature extraction

Dimensionality reduction

Block coordinate ascent

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.