Search Results for author:
Found 5 papers, 1 papers with code
Reproducing kernel Hilbert C*-module and kernel mean embeddings
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
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
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
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.
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning.
