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» A Hierarchical Latent Variable Model for Data Visualization
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GRC
2010
IEEE
13 years 9 months ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
ACL
2010
13 years 6 months ago
Latent Variable Models of Selectional Preference
This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modell...
Diarmuid Ó Séaghdha
CORR
2006
Springer
144views Education» more  CORR 2006»
13 years 8 months ago
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
JMLR
2010
157views more  JMLR 2010»
13 years 3 months ago
Combining Experiments to Discover Linear Cyclic Models with Latent Variables
We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relatio...
Frederick Eberhardt, Patrik O. Hoyer, Richard Sche...
JMLR
2010
141views more  JMLR 2010»
13 years 3 months ago
Hierarchical Gaussian Process Regression
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while o...
Sunho Park, Seungjin Choi