44 citations · 126 across the 16 of their papers we have counts for
16 papers
Reduced-Rank Estimation for Ill-Conditioned Stochastic Linear Model with High Signal-to-Noise Ratio
Tomasz Piotrowski, Isao Yamada
Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y=Hx+\sqrt{epsilon}n. In practical…
Generalized Left-Localized Cayley Parametrization for Optimization with Orthogonality Constraints
Kieta Kume, Isao Yamada
We present a reformulation of optimization problems over the Stiefel manifold by using a Cayley-type transform, named the generalized left-localized Cayley transform, for the Stief…
A Unified Framework for Solving a General Class of Nonconvexly Regularized Convex Models
Yi Zhang, Isao Yamada
Recently, several nonconvex sparse regularizers which can preserve the convexity of the cost function have received increasing attention. This paper proposes a general class of suc…
Adaptive Localized Cayley Parametrization for Optimization over Stiefel Manifold
Keita Kume, Isao Yamada
We present an adaptive parametrization strategy for optimization problems over the Stiefel manifold by using generalized Cayley transforms to utilize powerful Euclidean optimizatio…
An Inexact Proximal Linearized DC Algorithm with Provably Terminating Inner Loop
Yi Zhang, Isao Yamada
Standard approaches to difference-of-convex (DC) programs require exact solution to a convex subproblem at each iteration, which generally requires noiseless computation and infini…
Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators -- Enhancements of SVM and Lasso
Isao Yamada, Masao Yamagishi
The breakthrough ideas in the modern proximal splitting methodologies allow us to express the set of all minimizers of a superposition of multiple nonsmooth convex functions as the…
Linearly-involved Moreau-Enhanced-over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression
Masahiro Yukawa, Hiroyuki Kaneko, Kyohei Suzuki +1
We present an efficient mathematical framework based on the linearly-involved Moreau-enhanced-over-subspace (LiMES) model. Two concrete applications are considered: sparse modeling…
A Convexly Constrained LiGME Model and Its Proximal Splitting Algorithm
Wataru Yata, Masao Yamagishi, Isao Yamada
For the sparsity-rank-aware least squares estimations, the LiGME (Linearly involved Generalized Moreau Enhanced) model was established recently in [Abe, Yamagishi, Yamada, 2020] to…
Approximate Simultaneous Diagonalization of Matrices via Structured Low-Rank Approximation
Riku Akema, Masao Yamagishi, Isao Yamada
Approximate Simultaneous Diagonalization (ASD) is a problem to find a common similarity transformation which approximately diagonalizes a given square-matrix tuple. Many data scien…
A Hierarchical Convex Optimization for Multiclass SVM Achieving Maximum Pairwise Margins with Least Empirical Hinge-Loss
Yunosuke Nakayama, Masao Yamagishi, Isao Yamada
In this paper, we formulate newly a hierarchical convex optimization for multiclass SVM achieving maximum pairwise margins with least empirical hinge-loss. This optimization proble…
Linearly Involved Generalized Moreau Enhanced Models and Their Proximal Splitting Algorithm under Overall Convexity Condition
Jiro Abe, Masao Yamagishi, Isao Yamada
The convex envelopes of the direct discrete measures, for the sparsity of vectors or for the low-rankness of matrices, have been utilized extensively as practical penalties in orde…
Fejér-monotone hybrid steepest descent method for affinely constrained and composite convex minimization tasks
Konstantinos Slavakis, Isao Yamada
This paper introduces the Fejér-monotone hybrid steepest descent method (FM-HSDM), a new member to the HSDM family of algorithms, for solving affinely constrained minimization task…
Compositions and Convex Combinations of Averaged Nonexpansive Operators
Patrick L. Combettes, Isao Yamada
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…
Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient Projection and Krylov Subspace Techniques
R. C. de Lamare, M. Yukawa, I. Yamada
In this paper, we propose a novel reduced-rank adaptive filtering algorithm by blending the idea of the Krylov subspace methods with the set-theoretic adaptive filtering framework.…
Performance of the stochastic MV-PURE estimator in highly noisy settings
Tomasz Piotrowski, Isao Yamada
The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-PURE estimator) has been developed to provide linear estimation with robustness against high n…
The adaptive projected subgradient method constrained by families of quasi-nonexpansive mappings and its application to online learning
Konstantinos Slavakis, Isao Yamada
Many online, i.e., time-adaptive, inverse problems in signal processing and machine learning fall under the wide umbrella of the asymptotic minimization of a sequence of non-negati…