Subspace independent component analysis using vector kurtosis
No Thumbnail Available
File version
Author(s)
Sharma, Alok
Paliwal, Kuldip K
Paliwal, Kuldip K
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2006
Size
File type(s)
Location
License
Abstract
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.
Journal Title
Pattern Recognition
Conference Title
Book Title
Edition
Volume
39
Issue
Thesis Type
Degree Program
School
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Information systems