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» Unsupervised Greedy Learning of Finite Mixture Models
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TNN
2010
216views Management» more  TNN 2010»
13 years 2 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
ICPR
2000
IEEE
14 years 8 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo
SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
13 years 9 months ago
Simultaneous Unsupervised Learning of Disparate Clusterings
Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult...
Prateek Jain, Raghu Meka, Inderjit S. Dhillon
ICPR
2010
IEEE
13 years 6 months ago
Unsupervised Learning from Linked Documents
Documents in many corpora, such as digital libraries and webpages, contain both content and link information. In a traditional topic model which plays an important role in the uns...
Zhen Guo, Shenghuo Zhu, Yun Chi, Zhongfei Zhang, Y...
NIPS
2004
13 years 9 months ago
Sampling Methods for Unsupervised Learning
We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algo...
Robert Fergus, Andrew Zisserman, Pietro Perona