Machine learning based on sparsity-inducing regularization for matrices
| Project/Area Number |
22700138
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
TOMIOKA Ryota 東京大学, 大学院・情報理工学系研究科, 助教 (70518282)
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 機械学習 / スパース性 / 正則化 / 行列 / テンソル / スパース正則化 / スパース / 最適化 |
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| Research Abstract |
The outcomes of this research project can be summarized as follows:1. I have extended the dual augmented Lagrangian (DAL) algorithm to deal with spectral regularization for matrices and proposed the M-DAL algorithm(ICML2010). The super-linear convergence of DAL and M-DAL algorithms wasproven and published in JMLR. A review of DAL and related algorithms has beenpublished as part of “Optimization for Machine Learning” (MIT Press). I have alsomade the code publicly available to promote its use in wider research communities. 2. In order to deal with non-numerical data, I have extended DAL to handle multiplekernel learning with thousands of kernels. This was published in MachineLearning Journal. 3. I have extended the framework to spectral regularization for higher-order tensors and analyzed its statistical performance. This was presented at NIPS2011.
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Report
(4 results)
Research Products
(31 results)
