Studies of models and algorithms in machine learning via submodular optimization
| Project/Area Number |
16H06676
|
|---|
| Research Category |
Grant-in-Aid for Research Activity Start-up
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Mathematical informatics
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
Soma Tasuku 東京大学, 大学院情報理工学系研究科, 助教 (90784827)
|
|---|
| Project Period (FY) |
2016-08-26 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| 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)
|
|---|
| Keywords | 組合せ最適化 / 機械学習 / アルゴリズム |
|---|
| 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.
|
Report
(3 results)
Research Products
(11 results)
