Letters: Estimation of linear non-Gaussian acyclic models for latent factors

Authors:
Shohei Shimizu;Patrik O. Hoyer;Aapo Hyvärinen
Affiliations:
Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan and The Institute of Scientific and Industrial Research, Osaka U ...;Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, FIN-00014, Finland;Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, FIN-00014, Finland and Department of Mathematics and Statistics, University of Helsinki, F ...
Venue:
Neurocomputing
Year:
2009

Citing 5
Cited 1

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Learning the Structure of Linear Latent Variable Models

The Journal of Machine Learning Research
A Linear Non-Gaussian Acyclic Model for Causal Discovery

The Journal of Machine Learning Research
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

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.