Independent subspace analysis using k-nearest neighborhood distances

Authors:
Barnabás Póczos;András Lörincz
Affiliations:
Department of Information Systems, Eötvös Loránd University, Research Group on Intelligent Information Systems, Hungarian Academy of Sciences, Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Research Group on Intelligent Information Systems, Hungarian Academy of Sciences, Budapest, Hungary
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

A novel algorithin called independent subspace analysis (ISA) is introduced to estimate independent subspaccs. The algorithm solves tile ISA problein by estimating umlti-diinensional differential entropics. Two variants are examined, both of them utilize distances be tween the k-nearest neighbors of the sample points. Numerical simulations demonstrate the usefulness of the algorithms.