1 citations · 2 across the 2 of their papers we have counts for
5 papers
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…
Quadratic Programming Over Ellipsoids (with Applications to Constrained Linear Regression and Tensor Decomposition)
Anh-Huy Phan, Masao Yamagishi, Danilo Mandic +1
A novel algorithm to solve the quadratic programming problem over ellipsoids is proposed. This is achieved by splitting the problem into two optimisation sub-problems, quadratic pr…