Topographic Analysis of Correlated Components

H. Sasaki, M.U. Gutmann, H. Shouno, A. Hyvärinen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Independent component analysis (ICA) is a method to estimate components which are as statistically independent as possible. However, in many practical applications, the estimated components are not independent. Recent variants of ICA have made use of such residual dependencies to estimate an ordering (topography) of the components. Like in ICA, the components in those variants are assumed to be uncorrelated, which might be a rather strict condition. In this paper, we address this shortcoming. We propose a generative model for the source where the components can have linear and higher order correlations, which generalizes models in use so far. Based on the model, we derive a method to estimate topographic representations. In numerical experiments on artificial data, the new method is shown to be more widely applicable than previously proposed extensions of ICA. We learn topographic representations for two kinds of real data sets
Original languageEnglish
Title of host publicationProc. Asian Conference on Machine Learning (ACML)
PublisherJournal of Machine Learning Research - Proceedings Track
Pages365-378
Number of pages14
Publication statusPublished - 2012

Fingerprint

Dive into the research topics of 'Topographic Analysis of Correlated Components'. Together they form a unique fingerprint.

Cite this