Markov Random Walk Representations with Continuous Distributions
Chen-Hsiang Yeang, Martin Szummer
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.
PDF Link: /papers/03/p600-yeang.pdf
AUTHOR = "Chen-Hsiang Yeang
and Martin Szummer",
TITLE = "Markov Random Walk Representations with Continuous Distributions",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "600--607"