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Found 9 papers, 1 papers with code
An Analytic Solution for Kernel Adaptive Filtering
We provide an extended functional Wiener equation, and present a solution to this equation in an explicit, finite dimensional, data-dependent RKHS.
An Alternate View on Optimal Filtering in an RKHS
Kernel Adaptive Filtering (KAF) are mathematically principled methods which search for a function in a Reproducing Kernel Hilbert Space.
The Functional Wiener Filter
Because of the lack of congruence between the Gaussian RKHS and the space of time series, we compare performance of two pre-imaging algorithms: a fixed-point optimization (FWFFP) that finds and approximate solution in the RKHS, and a local model implementation named FWFLM.
A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data
Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima.
Correspondence of Deep Neural Networks and the Brain for Visual Textures
Deep convolutional neural networks (CNNs) trained on objects and scenes have shown intriguing ability to predict some response properties of visual cortical neurons.
Group-Wise Point-Set Registration Based on Renyi's Second Order Entropy
In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set.
Rate-Distortion Auto-Encoders
Here, we propose a learning algorithm for auto-encoders based on a rate-distortion objective that minimizes the mutual information between the inputs and the outputs of the auto-encoder subject to a fidelity constraint.
Information Theoretic Learning with Infinitely Divisible Kernels
In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices.
Measures of Entropy from Data Using Infinitely Divisible Kernels
In this way, capitalizing on both the axiomatic definition of entropy and on the representation power of positive definite kernels, the proposed measure of entropy avoids the estimation of the probability distribution underlying the data.
