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Multiple-Instance Learning (MIL) refers to the problem wherein each object is a bag consisting of multiple instances and only the bags' labels are provided. MIL data can contain irrelevant, redundant, and noisy components, which makes feature-extraction preprocessing essential for performance improvement. In this paper, we propose a Multiple-Instance Feature Extraction (MIFE) framework to design algorithms...
Many real-world face recognition applications can only provide single sample for each person, while most face recognition approaches require a large set of training samples, which leads to single sample per person (SSPP) problem. In this paper, we propose local structure based multi-phase collaborative representation classification (LS_MPCRC) to solve SSPP problem. By adopting the “divide-conquer-aggregate”...
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