2016 Fiscal Year Research-status Report
圧縮センシングとスパース表現の実時間処理技術の研究
| Project/Area Number |
16K00335
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| Research Institution | The University of Aizu |
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Principal Investigator |
丁 数学 会津大学, コンピュータ理工学部, 教授 (80372829)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | スパース表現 / 圧縮センシング / 実時間処理 |
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| Outline of Annual Research Achievements |
1)We proposed a novel dictionary learning algorithm with the log-regularizer and simultaneously with the coherence penalty based on proximal operators. Our proposed algorithm simply employs a decomposition scheme and alternating optimization, which transforms the overall problem into a set of single-vector variable sub-problems, with either one dictionary atom or one coefficient vector. Although the sub-problems are still nonsmooth and even nonconvex, remarkably they can be solved by proximal operators, and the closed-form solutions of the dictionary atoms and the coefficient vectors are obtained directly and explicitly. Was published in Digital Signal Processing (Elsevier), Vol. 63, No. 4, pp. 86-99.
2)We proposed two analysis dictionary learning algorithms for sparse representation with analysis model. The problem is formulated with the L1-norm regularizer and with two penalty terms on the analysis dictionary: the term of -log det (W’W) and the coherence penalty term. As the processing scheme, we employ a block coordinate descent framework, so that the overall problem is transformed into a set of minimizations of univariate sub-problems with respect to a single-vector variable. Each sub-problem is still nonsmooth, but it can be solved by a proximal operator and then the closed-form solutions can be obtained directly and explicitly. Was published in Neurocomputing (Elsevier), Vol. 239, C, pp. 165-180.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
According with the Plan for FY 2016, the research should be on “Scheme for real-time sparse representation and algorithms of sparse coding and dictionary leaning”. We have worked out two efficient algorithms as show in ”2017研究実績の概要”. Scheme for real-time sparse representation has also been progressed greatly.
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| Strategy for Future Research Activity |
1) We shall finish the part of Scheme for real-time sparse representation. The progress is going well.
2) We shall continue the research for new algorithms of sparse coding and dictionary leaning.
3) We shall apply our algorithms to Defect Detection on Thin-Wall Structure with ultrasonic transducers, an important engineering problem.
4) We shall apply our algorithms to Device-Free Localization with wireless transmitters/receivers, another important engineering problem.
5) We shall try to research on the relationship of dictionary learning problem and Deep learning.
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| Causes of Carryover |
国際会議での発表と雑誌での発表、そして、実験の実施などによる次の年度は計画より多くの経費使うことを予想したため。
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| Expenditure Plan for Carryover Budget |
各研究者用パーソナルコンピュータは現有のものを使用する予定であるため、追加が必要な研究のためのサーバや実験用GPUコンピュータのみが必要である。実験システムに関する改造修理費として、圧縮センシングとスパース表現の実時間処理技術についての評価テストなどの目的で、できるだけ現有のものを使用し節約する予定であるが、実験項目を変更するために改造と修理が必要である場合はそれための費用がかかる。
学会と雑誌での学術発表のとき登録費や旅費、Paper Chargeなどで研究費も使う予定である。また、謝金・研究補助としては主に大学院生の研究補助の費用となる。研究の評価と実験、特に、音源分離に関する心理テストでは多数の被験者を集めるため大学院生の研究補助が重要である。会津大学において、院生の時給が1,000円となっているので、この基準と必要時間数で支払う。
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