6 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 6 cited
Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces
Kan Li, Jose C. Principe
Kernel methods form a theoretically-grounded, powerful and versatile framework to solve nonlinear problems in signal processing and machine learning. The standard approach relies o…
cs.LG2019★ 5 cited
No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Features
Kan Li, Jose C. Principe
Kernel methods form a powerful, versatile, and theoretically-grounded unifying framework to solve nonlinear problems in signal processing and machine learning. The standard approac…
eess.SP2019
Functional Bayesian Filter
Kan Li, Jose C. Principe
We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). Applying kernel method and the rep…