K-hyperplanes clustering and its application to sparse component analysis

Zhaoshui He, Andrzej Cichocki, Shengli Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


In this paper the K-hyperplanes clustering problem is discussed and we present a K-hyperplanes clustering algorithm, which can be applied to sparse component analysis (SCA) for linear model X = AS +V , where X is a m by T matrix of observation, A is an unknown m by n basis matrix and S is an unknown n by T matrix of sparse sources. The proposed algorithm is suitable for a relaxed case when more than one source signal achieves significant value at any time instant. More precisely, in this paper we propose a new algorithm which is suitable for the case when the (m-1) source signals are simultaneously nonzero for sufficient number of samples, where m is the number of observation. In contrast to the conventional SCA algorithm which is based on the assumption that for each time, there is only one dominant component and others components are not significant. We assume that the sources can be only moderately sparse. However, the complexity of the algorithm is higher than that of the conventional SCA algorithms. We confirmed the validity and good performance of the proposed algorithm by computer simulation.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540464794, 9783540464792
Publication statusPublished - 2006
Externally publishedYes
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4232 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Neural Information Processing, ICONIP 2006
CityHong Kong


Dive into the research topics of 'K-hyperplanes clustering and its application to sparse component analysis'. Together they form a unique fingerprint.

Cite this