Markov Random Walk Representations with Continuous Distributions
Machine Learning
2012-12-12 v1 Machine Learning
Abstract
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the data density. We relate this diffusion equation to a path integral and derive the corresponding path probability measure. The framework is useful for incorporating continuous data densities and prior knowledge.
Cite
@article{arxiv.1212.2510,
title = {Markov Random Walk Representations with Continuous Distributions},
author = {Chen-Hsiang Yeang and Martin Szummer},
journal= {arXiv preprint arXiv:1212.2510},
year = {2012}
}
Comments
Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)