PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimation of linear non-Gaussian acyclic models for latent factors
Shohei Shimizu, Patrik Hoyer and Aapo Hyvärinen
Neurocomputing Volume 72, pp. 2024-2027, 2009. ISSN 0925-2312

Abstract

Many methods have been proposed for discovery of causal relations among observed variables. But one often wants to discover causal relations among latent factors rather than observed variables. Some methods have been proposed to estimate linear acyclic models for latent factors that are measured by observed variables. However, most of the methods use data covariance structure alone for model identification, and this leads to a number of indistinguishable models. In this paper, we show that a linear acyclic model for latent factors is identifiable when the data are non-Gaussian.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6477
Deposited By:Patrik Hoyer
Deposited On:08 March 2010