A study on orthogonal matrix optimization and application to blind source separation problems
| Project/Area Number |
15500129
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | TOKYO INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
YAMADA Isao Tokyo Institute of Technology, Graduate School of Science and Engineering, Associate Professor, 大学院・理工学研究科, 助教授 (50230446)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2003: ¥2,600,000 (Direct Cost: ¥2,600,000)
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| Keywords | Orthogonal matrix / Semi-orthogonal matrix / Dual-Cayley Parametrization / Reduced Rank matrix / Low-rank linear estimator / Adaptive projected subgradient method / Blind interference reduction / Stereo acoustic echo canceling / ブラインド信号分離 / Stiefel manifold / Dual Cayley変換 |
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| Research Abstract |
In this research project, we studied the blind signal processing and its related problems including (i)orthogonal matrix optimization for blind source separations, (ii)quadratic matrix optimization over the reduced rank matrices, (iii)convex optimization over the fixed point set of quasi-nonexpansive mapping, (iv)asymptotic minimization of sequence of convex functions and its unified view for adaptive filtering problems, (v)blind multiple access interfererence reduction problem for DS/CDMA systems, (vi)stereophonic acoustic echo canceling problem.
Mathematically, the above problems are callenging optimization problems which can be classified as follows. The problems on (i)and (ii)are, optimization problems over certain nonconvex constraint sets. The algorithm developed in (iii)answers the question posed by V.Vasin in 1995. The algorithm (adaptive projected subgradient method) developed in (iv)can solve a generalized optimization problem, where the cost function itself keeps changing during the whole process. The problems (v)and (vi)have been tackled by applying the adaptive projected subgradient method
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Report
(3 results)
Research Products
(44 results)
