| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
Recently, submodular optimization -- a branch of combinatorial optimization -- has attracted interests in the machine learning community. In this project, we studied submodular optimization and its applications to machine learning, and obtained the following results:
1. We developed new approximation guarantee based on the concept of discrete convexity, which improves previous approaches based on the concept of curvature.
2. For dictionary learning (a problem studied in compressed sensing and machine learning), we devise a new combinatorial algorithm.
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