Learning Generative Models of Similarity Matrices

Autor:	Rosales, Romer, Frey, Brendan J.
Rok vydání:	2012
Předmět:	Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1212.2494 Zobrazit plný text záznamu View this record from Arxiv