Search Results for author: Yuka Hashimoto

Found 5 papers, 1 papers with code

Reproducing kernel Hilbert C*-module and kernel mean embeddings

no code implementations27 Jan 2021 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures

no code implementations29 Jul 2020 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara

Kernel mean embedding (KME) is a powerful tool to analyze probability measures for data, where the measures are conventionally embedded into a reproducing kernel Hilbert space (RKHS).

Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$-Modules and Applications

no code implementations2 Mar 2020 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

no code implementations9 Sep 2019 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara

In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating the operators using finite data, with consideration of the unboundedness of operators.

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