Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Yamagishi, Masao"'
Autor:
Yamada, Isao, Yamagishi, Masao
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 fixed point set of computable nonexpansive operators. In this paper,
Externí odkaz:
http://arxiv.org/abs/2206.15019
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 use certain nonconvex enhancements of linearly involved convex regula
Externí odkaz:
http://arxiv.org/abs/2105.02994
Approximate Simultaneous Diagonalization (ASD) is a problem to find a common similarity transformation which approximately diagonalizes a given square-matrix tuple. Many data science problems have been reduced into ASD through ingenious modelling. Fo
Externí odkaz:
http://arxiv.org/abs/2010.06305
In this paper, we formulate newly a hierarchical convex optimization for multiclass SVM achieving maximum pairwise margins with least empirical hinge-loss. This optimization problem is a most faithful as well as robust multiclass extension of an NP-h
Externí odkaz:
http://arxiv.org/abs/2004.08180
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 order to compute a globally optimal solution of the corresponding regulari
Externí odkaz:
http://arxiv.org/abs/1910.10337
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 programming over a sphere and orthogonal projection. Next, an augmented-
Externí odkaz:
http://arxiv.org/abs/1711.04401
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Autor:
Phan, Anh-Huy1,2 (AUTHOR) a.phan@skoltech.ru, Yamagishi, Masao3 (AUTHOR), Mandic, Danilo4 (AUTHOR), Cichocki, Andrzej1,2,5,6 (AUTHOR)
Publikováno v:
Neural Computing & Applications. Jun2020, Vol. 32 Issue 11, p7097-7120. 24p.
Akademický článek
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