| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
In this study, we have proposed two novel methods for classifying features represented in a form of matrix or tensor; one is a matrix classifier, and the other is a similarity measure between the feature tensors which is used for exemplar-based classification. The proposed method fast optimizes the matrix classifier by minimizing classification errors as well as a matrix rank to produce a low-rank classifier of high generalization performance. Such low-rank classifier also facilitates to physically interpret the classifier weights for further analysis. The proposed similarity measure is based on partial matching of pair-wise feature tensors. It automatically extracts common patterns shared by those feature tensors and thereby produces effective similarity in disregard of noisy background patterns. In the experiments on various visual recognition tasks, the proposed methods exhibited favorable performance.
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