Yukawa, Masahiro



Faculty of Science and Technology, Department of Electronics and Electrical Engineering (Yagami)



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  • 数理的基盤に立脚した新しい信号処理パラダイムの構築を目指しています. 数理科学で蓄積された知見を活用することで,信号処理の諸問題を見通し良く解決することが目的です. 主に,不動点近似・凸解析を利用した適応信号処理アルゴリズム等の研究を行なっています. 新しい時代を切り拓く信号処理技術に繋げたいと考えます.

Career 【 Display / hide

  • 2005.04

    東京工業大学, 日本学術振興会特別研究員 DC2

  • 2006.10

    東京工業大学(英国国立ヨーク大学に留学), 日本学術振興会特別研究員 PD

  • 2007.04

    (独)理化学研究所, 基礎科学特別研究員

  • 2010.04

    新潟大学, 准教授

Academic Background 【 Display / hide

  • 1998.04

    Tokyo Institute of Technology, 工学部, 電気電子工学科

    University, Graduated

  • 2002.04

    Tokyo Institute of Technology, 理工学研究科, 集積システム専攻

    Graduate School, Completed, Master's course

  • 2004.04

    Tokyo Institute of Technology, 理工学研究科, 集積システム専攻

    Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide

  • 博士(工学), Tokyo Institute of Technology, Coursework, 2006.09

    A study of efficient adaptive filtering algorithms and their applications to acoustic and communication systems


Papers 【 Display / hide

  • Relaxed zero-forcing beamformer under temporally-correlated interference

    Kono T., Yukawa M., Piotrowski T.

    Signal Processing (Signal Processing)  190 2022.01

    ISSN  01651684

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    The relaxed zero-forcing (RZF) beamformer is a quadratically-and-linearly constrained minimum variance beamformer. The central question addressed in this paper is whether RZF performs better than the widely-used minimum variance distortionless response and zero-forcing beamformers under temporally-correlated interference. First, RZF is rederived by imposing an ellipsoidal constraint that bounds the amount of interference leakage for mitigating the intrinsic gap between the output variance and the mean squared error (MSE) which stems from the temporal correlations. Second, an analysis of RZF is presented for the single-interference case, showing how the MSE is affected by the spatio-temporal correlations between the desired and interfering sources as well as by the signal and noise powers. Third, numerical studies are presented for the multiple-interference case, showing the remarkable advantages of RZF in its basic performance as well as in its application to brain activity reconstruction from EEG data. The analytical and experimental results clarify that the RZF beamformer gives near-optimal performance in some situations.

  • Distributed Sparse Optimization with Minimax Concave Regularization

    Komuro K., Yukawa M., Cavalcante R.L.G.

    IEEE Workshop on Statistical Signal Processing Proceedings (IEEE Workshop on Statistical Signal Processing Proceedings)  2021-July   31 - 35 2021.07

    ISSN  9781728157672

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    We study the use of weakly-convex minmax concave (MC) regularizes in distributed sparse optimization. The global cost function is the squared error penalized by the MC regularizer. While it is convex as long as the whole system is overdetermined and the regularization parameter is sufficiently small, the local cost of each node is usually nonconvex as the system from local measurements are underdetermined in practical applications. The Moreau decomposition is applied to the MC regularizer so that the total cost takes the form of a smooth function plus the rescaled ℓ1 norm. We propose two solvers: the first applies the proximal gradient exact first-order algorithm (PG-EXTRA) directly to our cost, while the second is based on convex relaxation of the local costs to ensure convergence. Numerical examples show that the proposed approaches attain significant gains compared to the ℓ1 -based PG-EXTRA.

  • Kernel weights for equalizing kernel-wise convergence rates of multikernel adaptive filtering

    Jeong K., Yukawa M.

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences)  1 ( 6 ) 927 - 939 2021.06

    ISSN  09168508

     View Summary

    Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.

  • Robust Recovery of Jointly-Sparse Signals Using Minimax Concave Loss Function

    Suzuki K., Yukawa M.

    IEEE Transactions on Signal Processing (IEEE Transactions on Signal Processing)  69   669 - 681 2021

    ISSN  1053587X

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    We propose a robust approach to recovering jointly sparse signals in the presence of outliers. The robust recovery task is cast as a convex optimization problem involving a minimax concave loss function (which is weakly convex) and a strongly convex regularizer (which ensures the overall convexity). The use of the nonconvex loss makes the problem difficult to solve directly by the convex optimization methods even with the well-established firm shrinkage. We circumvent this difficulty by reformulating the problem via the Moreau decomposition so that the objective function becomes a sum of convex functions that can be minimized by the primal-dual splitting method. The parameter designs/ranges for the present specific case are derived to ensure the convergence. We demonstrate the remarkable robustness of the proposed approach against outliers by extensive simulations to the application of multi-lead electrocardiogram as well as synthetic data.

  • Outlier-robust kernel hierarchical-optimization RLS on a budget with affine constraints

    Slavakis K., Yukawa M.

    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings)  2021-June   5335 - 5339 2021

    ISSN  15206149

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    This paper introduces a non-parametric learning framework to combat outliers in online, multi-output, and nonlinear regression tasks. A hierarchical-optimization problem underpins the learning task: Search in a reproducing kernel Hilbert space (RKHS) for a function that minimizes a sample average ℓp-norm (1 ≤ p ≤ 2) error loss defined on data contaminated by noise and outliers, under affine constraints defined as the set of minimizers of a quadratic loss on a finite number of faithful data devoid of noise and outliers (side information). To surmount the computational obstacles inflicted by the choice of loss and the potentially infinite dimensional RKHS, approximations of the ℓp-norm loss, as well as a novel twist of the criterion of approximate linear dependency are devised to keep the computational-complexity footprint of the proposed algorithm bounded over time. Numerical tests on datasets showcase the robust behavior of the advocated framework against different types of outliers, under a low computational load, while satisfying at the same time the affine constraints, in contrast to the state-of-the-art methods which are constraint agnostic.

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Papers, etc., Registered in KOARA 【 Display / hide

Reviews, Commentaries, etc. 【 Display / hide

  • A New Stream of Nonlinear Adaptive Signal Processing Technique : An Application of Reproducing Kernel


    電子情報通信学会誌 (電子情報通信学会)  97 ( 10 ) 876 - 882 2014.10

    Article, review, commentary, editorial, etc. (scientific journal), Single Work

Presentations 【 Display / hide

  • Online Learning in L2 Space with Multiple Gaussian Kernels

    Motoya Ohnishi

    European Signal Processing Conference, 


  • Distributed Nonlinear Regression Using In-Network Processing With Multiple Gaussian Kernels

    Ban-Sok Shin

    IEEE International Workshop on Signal Processing Advances in Wireless Communications, 


  • Automatic shrinkage tuning based on a system-mismatch estimate for sparsity-aware adaptive filtering

    Masao Yamagishi, Yukawa Masahiro, and Isao Yamada

    International Conference on Acoustic, Speech and Signal Processing, 


    Oral presentation (general)

  • Projection-based dual averaging for stochastic sparse optimization

    Asahi Ushio and Masahiro Yukawa

    International Conference on Acoustic, Speech and Signal Processing, 


    Oral presentation (general)

  • Complex NMF with the generalized Kullback-Leibler divergence

    Hirokazu Kameoka, Hideaki Kagami, and Masahiro Yukawa

    International Conference on Acoustic, Speech and Signal Processing, 


    Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide

  • 正則化機能強化による超ロバスト推定法の開拓と一般化:信号処理・機械学習への応用


    MEXT,JSPS, Grant-in-Aid for Scientific Research, 基盤研究(B), Principal investigator

  • 不完全性を持つ非線形多重スケールデータのためのオンライン解析法の開発と実応用


    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Principal investigator

  • 再生核適応フィルタの解析と高性能アルゴリズム開発


    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

Awards 【 Display / hide

  • JSPS Prize

    湯川正裕, 2022.02, 日本学術振興会

    Type of Award: Other

  • 平成26年度科学技術分野の文部科学大臣表彰 若手科学者賞

    湯川正裕, 2014.04, 文部科学省, 新世代情報通信システムのための適応信号処理の研究

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  • 船井学術賞

    湯川正裕, 2016.04, 公益財団法人船井情報科学振興財団, 新世代情報通信システムのための適応信号処理アルゴリズムの研究

    Type of Award: Award from publisher, newspaper, foundation, etc.

  • KDDI財団賞(優秀研究賞)

    湯川正裕, 2015.03, 公益財団法人KDDI財団, カーネル学習と超高分解能ビームフォーマ法

    Type of Award: Award from publisher, newspaper, foundation, etc.

  • 電気通信普及財団賞テレコムシステム技術賞

    湯川正裕, 2014.03, 公益財団法人電気通信普及財団, Multikernel Adaptive Filtering

    Type of Award: Award from publisher, newspaper, foundation, etc.

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Courses Taught 【 Display / hide











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Memberships in Academic Societies 【 Display / hide

  • IEEE



Committee Experiences 【 Display / hide

  • 2022.04

    IEEE Transactions on Signal Processing, Senior Area Editor

  • 2015.02

    IEEE Transactions on Signal Processing, Associate Editor, IEEE