Isao Yamada

Tokyo Institute of Technology, Edo, Tōkyō, Japan

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Publications (176)279.05 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Common Spatial Pattern (CSP) methods are widely used to extract the brain activity for brain machine interfacing (BMI) based on electroencephalogram (EEG). For each mental task, CSP methods estimate a covariance matrix of EEG signals and adopt the uniform average of the sample covariance matrices over trials. However, the uniform average is sensitive to outliers caused by e.g. unrelated brain activity. In this paper, we propose an improvement of the estimated covariance matrix utilized in CSP methods by reducing the influence of the outliers as well as guaranteeing positive definiteness. More precisely, our estimation is the projection of the uniform average onto the intersection of two convex sets: the first set is a special reduced dimensional subspace which alleviates the influence of the outliers; the second is the positive definite cone. A numerical experiment supports the effectiveness of the proposed technique.
  • Source
    Patrick L. Combettes, Isao Yamada
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    ABSTRACT: Properties of compositions and convex combinations of averaged nonexpansive operators are investigated and applied to the design of new fixed point algorithms in Hilbert spaces. An extended version of the forward-backward splitting algorithm for finding a zero of the sum of two monotone operators is obtained.
    Journal of Mathematical Analysis and Applications 07/2014; 425(1). DOI:10.1016/j.jmaa.2014.11.044 · 1.12 Impact Factor
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    ABSTRACT: This paper is concerned with the mean-square performance of the hyperslab-based adaptive projected subgradient method, a set theoretic estimation tool that has been successfully applied in a wide variety of signal processing tasks. Using energy-conservation arguments, general performance results are derived without restricting the regression data to being Gaussian or white. Numerical simulations are provided to illustrate the theoretical developments.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: For the nonlinear acoustic echo cancellation, we present an adaptive learning of the saturation effect of the amplifier and the room propagation in terms of the hard-clipping and the FIR system. The conventional learning algorithms are based on a gradient descent method, i.e., rely on local information, which results in a major drawback that the estimation of the hard-clipping is trapped in local minima. In this paper, we solve this drawback by exploiting global information embodied as a set including the desired hard-clipping with high-probability. The proposed adaptive learning of the hard-clipping is designed to track the sets with a projection-based algorithm. In the adaptive learning of the FIR system, we propose the use of the Huber loss function for the robustness against the error in the estimation of the hard-clipping. Numerical examples show that the proposed algorithm is never trapped in the local minima and has an excellent steady-state behavior.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Daichi Kitahara, Isao Yamada
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    ABSTRACT: Phase unwrapping is a reconstruction problem of the continuous phase function from its finite wrapped samples. Especially the two-dimensional phase unwrapping has been a common key for estimating many crucial physical information, e.g, the surface topography measured by interferometric synthetic aperture radar. However almost all two-dimensional phase unwrapping algorithms are suffering from either the path dependence or the excess smoothness of the estimated result. In this paper, to guarantee the path independence and the appropriate smoothness of the estimated result, we present a novel algebraic approach by combining the ideas in the algebraic phase unwrapping with techniques for a piecewise polynomial interpolation of two-dimensional finite data sequence.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Shunsuke Ono, Isao Yamada
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    ABSTRACT: This paper proposes to use the Total Generalized Variation (TGV) of second order in a constrained form for image processing, which we call the TGV constraint. The main contribution is twofold: i) we present a general form of convex optimization problems with the TGV constraint, which is, to the best of our knowledge, the first attempt to use TGV as a constraint and covers a wide range of problem formulations sufficient for image processing applications; and ii) a computationally-efficient algorithmic solution to the problem is provided, where we mobilize several recently-developed proximal splitting techniques to handle the complicated structured set, i.e., the TGV constraint. Experimental results illustrate the potential applicability and utility of the TGV constraint.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: Effective utilization of sparsity of the system to be estimated is a key to achieve excellent adaptive filtering performances. This can be realized by the adaptive proximal forward-backward splitting (APFBS) with carefully chosen parameters. In this paper, we propose a systematic parameter tuning based on a minimization principle of an unbiased MSE estimate. Thanks to the piecewise quadratic structure of the proposed MSE estimate, we can obtain its minimizer with low computational load. A numerical example demonstrates the efficacy of the proposed parameter tuning by its excellent performance over a broader range of SNR than a heuristic parameter tuning of the APFBS.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: We consider the problem of electroencephalography (EEG) and magnetoencephalography (MEG) source localization using beamforming techniques. Specifically, we propose a reduced-rank extension of the recently derived multi-source activity index (MAI), which itself is an extension of the classical neural activity index to the multi-source case. We show that, for uncorrelated dipole sources and any nonzero rank constraint, the proposed reduced-rank multi-source activity index (RR-MAI) achieves the global maximum when evaluated at the true source positions. Therefore, the RR-MAI can be used to localize multiple sources simultaneously. Furthermore, we propose another version of the RR-MAI which can be seen as a natural generalization of the proposed index to arbitrarily correlated sources. We present a series of numerical simulations showing that the RR-MAI can achieve a more precise source localization than the full-rank MAI in the case when the EEG/MEG forward model becomes ill-conditioned, which in our settings corresponds to the case of closely positioned sources and low signal-to-noise ratio.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: We propose to detect edges of reflections, which we call the REF-edges, from a single image via convex optimization. Our method is designed based on two observations on reflections: (i) reflections have almost monotone color and (ii) color around REF-edges varies smoothly. The first one can be translated into the property that gradients around REF-edges distribute linearly in the RGB color space, which we call the REF-linearity. The second one can be interpreted as follows: color differences around REF-edges are small; for an entry of REF-edges, gradients among its surrounding entries have small variance. Using the above properties, we characterize REF-edges as a solution of a constrained convex optimization problem. The optimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experiments using real-world images with reflections show the utility of our proposed method.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: Using a novel characterization of texture, we propose an image decomposition technique that can effectively decomposes an image into its cartoon and texture components. The characterization rests on our observation that the texture component enjoys a blockwise low-rank nature with possible overlap and shear, because texture, in general, is globally dissimilar but locally well patterned. More specifically, one can observe that any local block of the texture component consists of only a few individual patterns. Based on this premise, we first introduce a new convex prior, named the block nuclear norm (BNN), leading to a suitable characterization of the texture component. We then formulate a cartoon-texture decomposition model as a convex optimization problem, where the simultaneous estimation of the cartoon and texture components from a given image or degraded observation is executed by minimizing the total variation and BNN. In addition, patterns of texture extending in different directions are extracted separately, which is a special feature of the proposed model and of benefit to texture analysis and other applications. Furthermore, the model can handle various types of degradation occurring in image processing, including blur+missing pixels with several types of noise. By rewriting the problem via variable splitting, the so-called alternating direction method of multipliers becomes applicable, resulting in an efficient algorithmic solution to the problem. Numerical examples illustrate that the proposed model is very selective to patterns of texture, which makes it produce better results than state-of-the-art decomposition models.
    IEEE Transactions on Image Processing 03/2014; 23(3):1128-42. DOI:10.1109/TIP.2014.2299067 · 3.11 Impact Factor
  • Takehiko Mizoguchi, Isao Yamada
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    ABSTRACT: The m-dimensional Cayley-Dickson number system Am is a standard extension of real (m=1), complex (m=2), quaternion (m=22), octonion (m=23) and sedenion (m=24) etc. In this paper, we present a systematic algebraic translation of the Cayley-Dickson hypercomplex valued linear systems into a real vector valued linear model. This translation is designed by using jointly two new isomorphisms between real vector spaces and enables us to straightforwardly apply the well established schemes in real domain to problems for the hypercomplex linear model. We also clarify useful algebraic properties of the proposed translation. As an example of many potential algorithms through the proposed algebraic translation, we present Am-adaptive projected subgradient method ( Am-APSM) for Am valued adaptive system identification, and show that many hypercomplex adaptive filtering algorithms can be viewed as special cases of this algorithm. Moreover, we also apply the Am-APSM to nonlinear adaptive filtering by using the kernel trick. Numerical examples show that the effectiveness of the Am-APSM in many Cayley-Dickson valued linear system identification and nonlinear channel equalization problems.
    IEEE Transactions on Signal Processing 02/2014; 62(6). DOI:10.1109/TSP.2013.2296881 · 3.20 Impact Factor
  • Tuan Duong Nguyen, Isao Yamada
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    ABSTRACT: Recently, we presented a first deterministic discrete time (DDT) analysis of the normalized normalized projection approximation subspace tracking (Normalized PAST) algorithms, for estimating principal and minor components of an input signal. The analysis shows that the DDT systems of the Normalized PAST algorithms converge to the desired eigenvectors under certain sufficient conditions on the forgetting factor @b@?(0,1]. However, it has not yet been clarified whether the sufficient conditions can be relaxed or not for guaranteed convergence. In this paper, by characterizing the maximal ranges of the forgetting factor, we establish the necessary and sufficient conditions for convergence of the DDT systems of the Normalized PAST algorithms. The proposed maximal range of the forgetting factor, for the minor component estimation, is doubled from the range assumed in the first DDT analysis, while the proposed maximal range of the forgetting factor, for principal component estimation, achieves the full range (0, 1]. Numerical examples further confirm the results.
    Signal Processing 01/2014; 94:288-299. DOI:10.1016/j.sigpro.2013.06.017 · 2.24 Impact Factor
  • Shunsuke Ono, Isao Yamada
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    ABSTRACT: We propose a novel framework to obtain an artifact-free enlarged image from a given JPEG image. The proposed formulation based on a newly introduced JPEG image acquisition model realizes decompression and super-resolution interpolation simultaneously using multi-order total variation, so that we can drastically reduce artifacts appearing in JPEG images such as block noise and mosquito noise, without generating staircasing effect, which is typical in existing total variation-based JPEG decompression methods. We also present a computationally-efficient optimization scheme, derived as a special case of a primal-dual splitting type algorithm, for solving the convex optimization problem associated with the proposed formulation. Numerical examples show that the proposed method works effectively compared with existing methods.
    Proc. of IEEE ICIP; 09/2013
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    ABSTRACT: The contribution of this paper is three-fold: first, we propose a novel scheme for generalized minor subspace extraction by extending an idea of dimension reduction technique. The key of this scheme is the reduction of the problem for extracting the ith (i ≥ 2) minor generalized eigenvector of the original matrix pencil to that for extracting the first minor generalized eigenvector of a matrix pencil of lower dimensionality. The proposed scheme can employ any algorithm capable of estimating the first minor generalized eigenvector. Second, we propose a pair of such iterative algorithms and analyze their convergence properties in the general case where the generalized eigenvalues are not necessarily distinct. Third, by using these algorithms inductively, we present adaptive implementations of the proposed scheme for estimating an orthonormal basis of the generalized minor subspace. Numerical examples show that the proposed adaptive subspace extraction algorithms have better numerical stability than conventional algorithms.
    Multidimensional Systems and Signal Processing 09/2013; 24(3). DOI:10.1007/s11045-012-0172-9 · 1.58 Impact Factor
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    ABSTRACT: This letter establishes a novel analysis of the Adaptive Projected Subgradient Method (APSM) in the intersection of the stochastic and robust estimation paradigms. Utilizing classical worst-case bounds on the noise process, drawn from the robust estimation methodology, the present study demonstrates that the hyperslab-inspired version of the APSM generates a sequence of estimates which converges to a point located, with probability one, arbitrarily close to the estimand. Numerical tests and comparisons with classical time-adaptive algorithms corroborate the theoretical findings of the study.
    IEEE Signal Processing Letters 07/2013; 20(7):729-732. DOI:10.1109/LSP.2013.2257169 · 1.64 Impact Factor
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    ABSTRACT: The topic of this special issue deals with a subject matter that has been receiving immense attention from various research communities, and not only within the signal processing community. Discusses research and development in the area of the adaption and learning over complex network systems. Extensive research efforts on information processing over graphs exist within other fields such as statistics, computer science, optimization, control, economics, machine learning, biological sciences, and social sciences. Different fields tend to emphasize different aspects and challenges; nevertheless, opportunities for mutual cooperation are abundantly clear, and the role that signal processing plays in this domain is of fundamental importance.
    IEEE Signal Processing Magazine 05/2013; 30(3):14-15. DOI:10.1109/MSP.2013.2240191 · 4.48 Impact Factor
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    ABSTRACT: The articles in this special issue report on up-to-date advances in the broad area of information processing over graphs. Due to the highly cross-disciplinary nature of complex networks, the technical articles in this April 2013 issue of the IEEE Journal of Selected Topics in Signal Processing are coupled with valuable tutorial articles that appear in a second special issue, organized by the same Guest Editors, and which is published as the May 2013 issue of the IEEE Signal Processing Magazine. The survey articles in the magazine are meant to introduce readers to the main tools and concepts, while the more focused technical articles in J-STSP cover state-of-the-art results. Through this combination of tutorial and technical articles in both journals, readers will become better acquainted with the challenges and opportunities that the broader field of network science has to offer across the domains of information sciences, system science, computer science, social sciences,machine learning, and optimization theory. Complex networks represent a typical paradigm that helps demonstrate well how barriers among seemingly different disciplines are becoming more transparent.
    IEEE Journal of Selected Topics in Signal Processing 04/2013; 7(2):161-162. DOI:10.1109/JSTSP.2013.2246331 · 3.63 Impact Factor
  • Tuan Duong Nguyen, Isao Yamada
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    ABSTRACT: The main contributions of this paper are to propose and analyze fast and numerically stable adaptive algorithms for the generalized Hermitian eigenvalue problem (GHEP), which arises in many signal processing applications. First, for given explicit knowledge of a matrix pencil, we formulate two novel deterministic discrete-time (DDT) systems for estimating the generalized eigen-pair (eigenvector and eigenvalue) corresponding to the largest/smallest generalized eigenvalue. By characterizing a generalized eigen-pair as a stationary point of a certain function, the proposed DDT systems can be interpreted as natural combinations of the normalization and quasi-Newton steps for finding the solution. Second, we present adaptive algorithms corresponding to the proposed DDT systems. Moreover, we establish rigorous analysis showing that, for a step size within a certain range, the sequence generated by the DDT systems converges to the orthogonal projection of the initial estimate onto the generalized eigensubspace corresponding to the largest/smallest generalized eigenvalue. Numerical examples demonstrate the practical applicability and efficacy of the proposed adaptive algorithms.
    IEEE Transactions on Signal Processing 03/2013; 61(6):1404-1418. DOI:10.1109/TSP.2012.2234744 · 3.20 Impact Factor
  • Tomasz Piotrowski, Isao Yamada
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    ABSTRACT: The stochastic MV-PURE estimator has been developed to provide linear estimation robust to ill-conditioning, high noise levels, and imperfections in model knowledge. In this paper, we investigate the theoretical performance of the stochastic MV-PURE estimator under varying level of additive noise. More precisely, we prove that the mean-square-error (MSE) of this estimator in the low signal-to-noise (SNR) region is much smaller than that obtained with its full-rank version, the minimum-variance distortionless estimator, and that the gap in performance is the larger the higher the noise level. These results shed light on the excellent performance of the stochastic MV-PURE estimator in highly noisy settings obtained in simulations so far. We extend here previously conducted numerical simulations to demonstrate a new insight provided by results of this paper in practical applications.
    Journal of the Franklin Institute 03/2013; 351(6). DOI:10.1016/j.jfranklin.2014.03.012 · 2.26 Impact Factor
  • S Ono, I Yamada
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    ABSTRACT: This paper proposes a likelihood constrained optimization framework for Poisson image restoration. The likelihood constrained problem considered in this paper is the minimization of convex priors over the level set of the negative-log-likelihood function of the Poisson distribution. It has advantages in parameter selection compared with the minimization of the weighted sum of convex priors and the negative-log-likelihood function, which has been used in conventional methods. The level set is characterized as the fixed point set of a certain quasi-nonexpansive operator, which enables us to apply the hybrid steepest descent method to solve the constrained problem. The proposed framework not only can handle the level set of any convex function whose subgradient is available but also does not require any computationally-expensive procedure such as operator inversion and inner loop. Illustrative numerical examples are also presented.
    Proc. IEEE ICASSP; 01/2013

Publication Stats

2k Citations
279.05 Total Impact Points

Institutions

  • 1991–2014
    • Tokyo Institute of Technology
      • • Department of Communications and Integrated Sytems
      • • Electrical and Electronic Engineering Department
      Edo, Tōkyō, Japan
  • 2011
    • Fraunhofer Heinrich-Hertz-Institute HHI
      Berlín, Berlin, Germany
  • 2010
    • RIKEN
      • Laboratory for Mathematical Neuroscience
      Wako, Saitama-ken, Japan
  • 2008
    • University of Peloponnese
      • Department of Telecommunications Science and Technology
      Trípoli, Peloponnese, Greece
  • 2007
    • Harokopion University of Athens
      Athínai, Attica, Greece
  • 2006
    • Nagoya University
      • Department of Electrical Engineering and Computer Science
      Nagoya-shi, Aichi-ken, Japan