Uncertainty in Artificial Intelligence
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Learning Generative Models of Similarity Matrices
Romer Rosales, Brendan Frey
We describe a probabilistic (generative) view of affinity matrices along with inference algorithms for a subclass of problems associated with data clustering. This probabilistic view is helpful in understanding different models and algorithms that are based on affinity functions OF the data. IN particular, we show how(greedy) inference FOR a specific probabilistic model IS equivalent TO the spectral clustering algorithm.It also provides a framework FOR developing new algorithms AND extended models. AS one CASE, we present new generative data clustering models that allow us TO infer the underlying distance measure suitable for the clustering problem at hand. These models seem to perform well in a larger class of problems for which other clustering algorithms (including spectral clustering) usually fail. Experimental evaluation was performed in a variety point data sets, showing excellent performance
Keywords: Clustering, Approximate Inference, Generative Model, Spectral, Manifold, Dimensionali
Pages: 485-492
PS Link: http://www.psi.utoronto.ca/~romer/papers/Rosales_Frey_UAI03.ps
PDF Link: /papers/03/p485-rosales.pdf
AUTHOR = "Romer Rosales and Brendan Frey",
TITLE = "Learning Generative Models of Similarity Matrices",
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 = "485--492"

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