14 citations · 45 across the 10 of their papers we have counts for
6 papers · 1 filter
The Representation Jensen-Shannon Divergence
Jhoan K. Hoyos-Osorio, Luis G. Sanchez-Giraldo
Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due t…
DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies
Oscar Skean, Jhoan Keider Hoyos Osorio, Austin J. Brockmeier +1
We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying d…
The Representation Jensen-Rényi Divergence
Jhoan Keider Hoyos Osorio, Oscar Skean, Austin J. Brockmeier +1
We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence i…
Multiscale Principle of Relevant Information for Hyperspectral Image Classification
Yantao Wei, Shujian Yu, Luis Sanchez Giraldo +1
This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI…
Rate-Distortion Auto-Encoders
Luis G. Sanchez Giraldo, Jose C. Principe
A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning. Current efforts have been directed towards effective training of auto-encoder…
Information Theoretic Learning with Infinitely Divisible Kernels
Luis G. Sanchez Giraldo, Jose C. Principe
In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matric…