Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

12

PR
2006

favoriteEmaildiscussreport

95views more PR 2006»

Subspace independent component analysis using vector kurtosis

13 years 4 months ago

Subspace independent component analysis using vector kurtosis

Download maxwell.me.gu.edu.au

This discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components. One of the main advantages of the presented approach is its computational simplicity. The experiments have shown promising results in estimating subspace independent components. 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Alok Sharma, Kuldip K. Paliwal

Real-time Traffic

Kurtosis | PR 2006 | Subspace Ica Algorithm | Subspace Independent Components |

claim paper

Related Content

» A novel vector quantizationbased video summarization method using independent component an...

» Hierarchical Extraction of Independent Subspaces of Unknown Dimensions

» Bayesian Estimation of Overcomplete Independent Feature Subspaces for Natural Images

» A Class of Complex ICA Algorithms Based on the Kurtosis Cost Function

» Independent subspace analysis with prior information for fMRI data

» Extraction of a source signal whose kurtosis value lies in a specific range

» Subspaces of Spatially Varying Independent Components in fMRI

» Grammatical inference as a principal component analysis problem

» Denoising using local projective subspace methods

Post Info
More Details (n/a)

Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2006
Where	PR
Authors	Alok Sharma, Kuldip K. Paliwal

Comments (0)