Estimation of Multivariate Binary Density Using Orthonormal Functions.

Author(s):  
X. R. Chen ◽  
P. R. Krishnaiah ◽  
W. Q. Liang
Keyword(s):  
2010 ◽  
Vol 22 (9) ◽  
pp. 2285-2307 ◽  
Author(s):  
Hugo Larochelle ◽  
Yoshua Bengio ◽  
Joseph Turian

We investigate the problem of estimating the density function of multivariate binary data. In particular, we focus on models for which computing the estimated probability of any data point is tractable. In such a setting, previous work has mostly concentrated on mixture modeling approaches. We argue that for the problem of tractable density estimation, the restricted Boltzmann machine (RBM) provides a competitive framework for multivariate binary density modeling. With this in mind, we also generalize the RBM framework and present the restricted Boltzmann forest (RBForest), which replaces the binary variables in the hidden layer of RBMs with groups of tree-structured binary variables. This extension allows us to obtain models that have more modeling capacity but remain tractable. In experiments on several data sets, we demonstrate the competitiveness of this approach and study some of its properties.


1985 ◽  
Vol 16 (1) ◽  
pp. 162-172 ◽  
Author(s):  
Wen-Qi Liang ◽  
P.R. Krishnaiah

Author(s):  
Ranjeet Kumar Singh ◽  
Kisor Kumar Sahu ◽  
Ganesh Chalavadi ◽  
Ratnakar Singh ◽  
N. P. H. Padmanabhan
Keyword(s):  

1989 ◽  
Vol 31 (2) ◽  
pp. 178-186 ◽  
Author(s):  
X.R. Chen ◽  
P.R. Krishnaiah ◽  
W.W. Liang

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