Yukawa, Masahiro

写真a

Affiliation

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

Position

Professor

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Profile 【 Display / hide 】

  • 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．

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Career 【 Display / hide 】

  • 2005.04
    -
    2006.09

    東京工業大学, 日本学術振興会特別研究員 DC2

  • 2006.10
    -
    2007.03

    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
    -
    2010.03

    （独）理化学研究所, 基礎科学特別研究員

  • 2010.04
    -
    2013.03

    新潟大学, 准教授

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Academic Background 【 Display / hide 】

  • 1998.04
    -
    2002.03

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

    University, Graduated

  • 2002.04
    -
    2004.03

    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
    -
    2006.09

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

    Graduate School, Completed, Doctoral course

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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

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Papers 【 Display / hide 】

  • Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields

    Koyakumaru T., Yukawa M., Pavez E., Ortega A.

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences)  E106A ( 1 ) 23 - 34 2023.01

    ISSN  09168508

    　View Summary

    This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the `1 norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the `1 norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than `1 for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau’s decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.

  • Linearly-Involved Moreau-Enhanced-Over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression

    Yukawa M., Kaneko H., Suzuki K., Yamada I.

    IEEE Transactions on Signal Processing (IEEE Transactions on Signal Processing)  71   1232 - 1247 2023

    ISSN  1053587X

    　View Summary

    We present an efficient mathematical framework to derive promising methods that enjoy 'enhanced' desirable properties. The popular minimax concave penalty for sparse modeling subtracts, from the ℓ 1 norm, its Moreau envelope, inducing nearly unbiased estimates and thus yielding considerable performance enhancements. To extend it to underdetermined linear systems, we propose the projective minimax concave penalty, which leads to 'enhanced' sparseness over the input subspace. We also present a promising regression method which has an 'enhanced' robustness and substantial stability by distinguishing outlier and noise explicitly. The proposed framework, named the linearly-involved Moreau-enhanced-over-subspace (LiMES) model, encompasses those two specific examples as well as two others: stable principal component pursuit and robust classification. The LiMES function involved in the model is an 'additively nonseparable' weakly convex function, while the 'inner' objective function to define the Moreau envelope is 'separable'. This mixed nature of separability and nonseparability allows an application of the LiMES model to the underdetermined case with an efficient algorithmic implementation. Two linear/affine operators play key roles in the model: one corresponds to the projection mentioned above and the other takes care of robust regression/classification. A necessary and sufficient condition for convexity of the smooth part of the objective function is studied. Numerical examples show the efficacy of LiMES in applications to sparse modeling and robust regression.

  • Sparse Stable Outlier-Robust Signal Recovery Under Gaussian Noise

    Suzuki K., Yukawa M.

    IEEE Transactions on Signal Processing (IEEE Transactions on Signal Processing)  71   372 - 387 2023

    ISSN  1053587X

    　View Summary

    This paper presents a novel framework for sparse robust signal recovery integrating the sparse recovery using the minimax concave (MC) penalty and robust regression called sparse outlier-robust regression (SORR) using the MC loss. While the proposed approach is highly robust against huge outliers, the sparseness of estimates can be controlled by taking into consideration a tradeoff between sparseness and robustness. To accommodate the prior information about additive Gaussian noise and outliers, an auxiliary vector to model the noise is introduced. The remarkable robustness and stability come from the use of the MC loss and the squared \ell {2} penalty of the noise vector, respectively. In addition, the simultaneous use of the MC and squared \ell {2} penalties of the coefficient vector leads to a certain remarkable grouping effect. The necessary and sufficient conditions for convexity of the smooth part of the cost are derived under a certain nonempty-interior assumption via the product space formulation using the linearly-involved Moreau-enhanced-over-subspace (LiMES) framework. The efficacy of the proposed method is demonstrated by simulations in its application to speech denoising under highly noisy environments as well as to toy problems.

  • Robust Online Multiuser Detection: A Hybrid Model-Data Driven Approach

    Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahiro Yukawa, Slawomir Stanczak

    IEEE TRANSACTIONS ON SIGNAL PROCESSING (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC)  71   2103 - 2117 2023

    ISSN  1053587X

    　View Summary

    We study the problem of robust online multiuser detection in the context of non-orthogonal multiple access. The optimal multiuser detector (in terms of the uncoded bit error rate) is the nonlinear maximum a posteriori (MAP) filter. Learning good nonlinear functions of this type (e.g., with deep neural networks) typically requires a large number of training samples and complicated signal processing, which poses a fundamental problem in dynamic wireless environments. Furthermore, compared with linear approaches, nonlinear filters are generally less robust against changes in the environment. To overcome these problems, we first show that the optimal MAP filter belongs to reproducing kernel Hilbert spaces (RKHSs) associated with Gaussian kernels whose widths satisfy a condition related to the standard deviation of the receiver noise. Second, we show how to approximate the optimal MAP filter with a computationally simple signal processing algorithm using a relatively small number of training samples. Third, to make the filter robust against changes in the wireless environment, we design a partially linear filter in the sum of an RKHS containing the MAP filter and an RKHS of a linear kernel. Finally, based on this partially linear design, we propose a multiuser detection framework that, in contrast to some state-of-the-art approaches, has the following desired features: (i) it has low complexity; (ii) it can work with small sample sets; (iii) it shows better robustness than a purely nonlinear receiver; and (iv) it does not require user parameter estimation (e.g., channels), which is prone to errors and may not be possible in certain scenarios.

  • Relaxed zero-forcing beamformer under temporally-correlated interference

    Kono T., Yukawa M., Piotrowski T.

    Signal Processing (Signal Processing)  190 2022.01

    ISSN  01651684

    　View Summary

    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.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

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

    YUKAWA MASAHIRO

    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

  • A Variable-Metric NLMS Algorithm for Sparse Systems and Colored Inputs

    Toda Osamu, Yukawa Masahiro, Sasaki Shigenobu

    信号処理シンポジウム講演論文集 ([電子情報通信学会信号処理研究専門委員会])  27   527 - 530 2012.11

    ISSN  1881-4654

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Presentations 【 Display / hide 】

  • Online Learning in L2 Space with Multiple Gaussian Kernels

    Motoya Ohnishi

    European Signal Processing Conference, 

    2017.08

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

    Ban-Sok Shin

    IEEE International Workshop on Signal Processing Advances in Wireless Communications, 

    2017.07

  • 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, 

    2017.03
    , 

    Oral presentation (general)

  • Projection-based dual averaging for stochastic sparse optimization

    Asahi Ushio and Masahiro Yukawa

    International Conference on Acoustic, Speech and Signal Processing, 

    2017.03
    , 

    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, 

    2017.03
    , 

    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

    2022.04
    -
    2026.03

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

  • 階層構造を持つ確率的凸最適化アルゴリズムの開発と大規模機械学習問題への応用

    2019.04
    -
    2024.03

    日本学術振興会, 科学研究費助成事業 基盤研究(B), 基盤研究(B), No Setting

    　View Summary

    階層構造を持つ凸最適化アルゴリズムを機械学習に効果的に応用するための基盤構築に取り組み、その成果を凸最適化理論の世界的権威(Bauschke等)が企画編集した研究書収録の査読付き招待論文(77頁)の形で世界に発信することができた。この論文には「非拡大写像の不動点集合上の凸最適化法(ハイブリッド最急降下法)」と「単調作用素の近接分解法」の融合による「階層構造を持つ凸最適化問題の解法」とその応用法を示したものであり、1995年以来の未解決問題「誤識別サンプル数を最小にする線形識別器の中から最大マージンを達成する特別な線形識別器を選択する問題」に対する実用的近似解法を与えることに成功している。なお、この方針を多クラス識別問題に拡張することにも成功しており、経験ヒンジ損失最小を達成する全ての多クラス線形識別器の中で、全てのクラス間マージンを一様最大化する線形識別器実現問題を世界ではじめて定式化し、その解法を実現している。その他にも「非負値成分制約を満たす超複素行列」の解集合を計算可能な非拡大写像の不動点集合によって表現できることを示すと共に、スパースな情報表現が求められる逆問題の切り札として注目されている「一般化モローエンベロープ型モデル」の解集合も計算可能な非拡大写像の不動点集合によって表現できること示し、各々の問題の最適解への収束が理論的に保証されたアルゴリズムの開発に成功している。さらに、複数行列の近似同時対角化問題を構造制約付き低ランク行列補完問題に帰着する新解法のアイディアを与え、その解集合が非線形写像の不動点集合によって近似できることを示している。

  • 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

    2018.04
    -
    2022.03

    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

    2015.04
    -
    2019.03

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

  • Construction of Machine Learning Algorithm Based on Fixed Point Theory and Its Application to Wireless Communication Systems

    2008
    -
    2011

    Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), YUKAWA Masahiro, Grant-in-Aid for Young Scientists (B), No Setting

    　View Summary

    Based on fixed point theory, we have obtained the following outcomes.(1) We have derived a reduced-rank adaptive algorithm, analyzed its convergence properties, and shown that it ameliorates the convergence rate considerably with low computational complexity.(2) We have introduced the new concept of time-varying metric producing a general framework to analyze the convergence properties of a number of adaptive algorithms in a unified fashion.(3) We have imported the concept of feasibility splitting to adaptive signal processing, derived an efficient adaptive algorithm to exploit a variety of information that is expressed in multiple domains, analyzed its convergence properties, and shown its efficacy in SIMO/MIMO wireless communication systems.

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Awards 【 Display / hide 】

  • DoCoMo Mobile Communications Award

    Masahiro Yukawa, 2023.10, 高信頼な適応信号処理アルゴリズムの開発と応用

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

  • JSPS Prize

    湯川正裕, 2022.02, 日本学術振興会, モデル選択に基づく非線形推定による革新的適応信号処理分野の開拓

    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

    　View Description

    適応信号処理アルゴリズムの収束速度、計算量、外乱へのロバスト性の間に存在するトレードオフ問題は、長年、未解決であった。また、再生核を用いた非線形手法が提案されていたが、時間変動を伴う未知系に適合した再生核を事前に設計することは困難であった。
    氏は、凸解析・不動点理論を駆使した独創的な技術を提案し、適応信号処理アルゴリズムのトレードオフ問題を解消することに成功した。さらに、再生核理論とスパース最適化の融合により、再生核設計とシステム推定を同時に自動化し、再生核と基底系の適応的精錬機能を持つ革新的な非線形適応アルゴリズムを世界に先駆けて提案した。本研究成果は、信号処理・機械学習分野で国際的に大きなインパクトを与えており、宇宙工学・脳科学・気象学など様々な隣接分野の発展に寄与するものと期待される。

  • 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.

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Courses Taught 【 Display / hide 】

  • TOPICS IN ELECTRICAL AND ELECTRONICS ENGINEERING

    2024

  • SEMINOR IN ELECTRONICS AND INFOTMATION ENGINEERING(2)

    2024

  • RECITATION IN ELECTRONICS AND INFORMATION ENGINEERING

    2024

  • MATHEMATICS IN ELECTRICAL ENGINEERING

    2024

  • LABORATORIES IN SCIENCE AND TECHNOLOGY

    2024

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Memberships in Academic Societies 【 Display / hide 】

  • IEICE

    　

  • IEEE

    　
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Committee Experiences 【 Display / hide 】

  • 2022.04
    -
    Present

    IEEE Transactions on Signal Processing, Senior Area Editor

  • 2015.02
    -
    2019.01

    IEEE Transactions on Signal Processing, Associate Editor, IEEE

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