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SubMIL: Discriminative subspaces for multi-instance learning
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DOI：10.1016/j.neucom.2015.08.089.png)
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Abstract
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As an important learning scheme for Multi-Instance Learning (MIL), the Instance Prototype (IP) selection-based MIL algorithms transform bags into a new instance feature space and achieve impressed classification performance. However, the number of IPs in the existing algorithms linearly increases with the scale of the training data. The performance and efficiencies of these algorithms are easily limited by the high dimension and noise when facing a large scale of training data. This paper proposes a discriminative subspaces-based instance prototype selection method that is suitable for reducing the computation complexity for large scale training data. In the proposed algorithm, we introduce the low-rank matrix recovery technique to find two discriminative and clean subspaces with less noise; then present a l(2,1) norm-based self-expressive sparse coding model to select the most representative instances in each subspace. Experimental results on several data sets show that our algorithm achieves superior and stable performance but with lower dimension compared with other IP selection strategies. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Multi-instance learning
Low rank
Subspace
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