Title: Likelihood-based regularization and differential log-likelihood in kernel-based topographic map formation
Authors: Van Hulle, Marc # ×
Issue Date: 2004
Host Document: Proceedings pages:3-11
Conference: IEEE Workshop on Machine Learning for Signal Processing edition:14 location:São Luis, Brazil date:Sep 29 - Oct 1, 2004
Abstract: Two new principles for kernel-based density estimation and kernel-based topographic map formation are introduced: likelihood-based regularization and differential log-likelihood. The former makes that every kernel has an equal probability of generating data points. The differential log-likelihood is an unbiased metric with which to judge the quality of the density estimate. We apply these principles to kernel-based topographic map formation based on log-likelihood maximization. We restrict ourselves to Gaussian kernels and homogeneous, homoscedastic mixings. We show that the negative log-likelihood equals the quantization error of the map up to a scale factor
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science