8 citations · 9 across the 2 of their papers we have counts for
5 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…
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…
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…
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…
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…