SOMICA - An Application of Self-organizing Maps to Geometric Independent Component Analysis

Fabian J. Theis; Carlos G. Puntonet; Elmar W. Lang

SOMICA - An Application of Self-organizing Maps to Geometric Independent Component Analysis

Fabian J. Theis
, Carlos G. Puntonet
, Elmar W. Lang

University of Regensburg
University of Granada

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.

Original language	English
Pages	1318-1323
Number of pages	6
State	Published - 2003
Externally published	Yes
Event	International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: 20 Jul 2003 → 24 Jul 2003

Conference

Conference	International Joint Conference on Neural Networks 2003
Country/Territory	United States
City	Portland, OR
Period	20/07/03 → 24/07/03

Cite this

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title = "SOMICA - An Application of Self-organizing Maps to Geometric Independent Component Analysis",

abstract = "Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.",

author = "Theis, \{Fabian J.\} and Puntonet, \{Carlos G.\} and Lang, \{Elmar W.\}",

year = "2003",

language = "English",

pages = "1318--1323",

note = "International Joint Conference on Neural Networks 2003 ; Conference date: 20-07-2003 Through 24-07-2003",

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TY - CONF

T1 - SOMICA - An Application of Self-organizing Maps to Geometric Independent Component Analysis

AU - Theis, Fabian J.

AU - Puntonet, Carlos G.

AU - Lang, Elmar W.

PY - 2003

Y1 - 2003

N2 - Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.

AB - Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.

UR - https://www.scopus.com/pages/publications/0141572235

M3 - Paper

AN - SCOPUS:0141572235

SP - 1318

EP - 1323

T2 - International Joint Conference on Neural Networks 2003

Y2 - 20 July 2003 through 24 July 2003

ER -

SOMICA - An Application of Self-organizing Maps to Geometric Independent Component Analysis

Abstract

Conference

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