780 citations · 1.8k across the 76 of their papers we have counts for
12 papers · 1 filter
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…
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…
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image
Chi Ding, Zheng Cao, Matthew S. Emigh +6
To fully understand interactions between marine hydrokinetic (MHK) equipment and marine animals, a fast and effective monitoring system is required to capture relevant information…
Unsupervised decoding of spinal motor neuron spike trains for estimating hand kinematics following targeted muscle reinnervation
Arash Andalib, Dario Farina, Ivan Vujaklija +4
The performance of upper-limb prostheses is currently limited by the relatively poor functionality of unintuitive control schemes. This paper proposes to extract, from multichannel…
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer +3
Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization…
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…
Correntropy Based Robust Decomposition of Neuromodulations
Shailaja Akella, Jose C. Principe
Neuromodulations as observed in the extracellular electrical potential recordings obtained from Electroencephalograms (EEG) manifest as organized, transient patterns that differ st…
A New Uncertainty Framework for Stochastic Signal Processing
Rishabh Singh, Jose C. Principe
The fields of signal processing and information theory have evolved with the goal of developing formulations to extract intrinsic information from limited amount of data. When one…
Minimum Error Entropy Kalman Filter
Badong Chen, Lujuan Dang, Yuantao Gu +2
To date most linear and nonlinear Kalman filters (KFs) have been developed under the Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In order to…
Maximum Correntropy Criterion with Variable Center
Badong Chen, Xin Wang, Yingsong Li +1
Correntropy is a local similarity measure defined in kernel space and the maximum correntropy criterion (MCC) has been successfully applied in many areas of signal processing and m…
Reduction of Markov Chains using a Value-of-Information-Based Approach
Isaac J. Sledge, Jose C. Principe
In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comp…
An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations
Isaac J. Sledge, Jose C. Principe
Radial-basis-function networks are traditionally defined for sets of vector-based observations. In this short paper, we reformulate such networks so that they can be applied to adj…