A Neural Network Approach for Subspace Decomposition and Its Dimension Estimation

Author

Jiang-wei, Ge ; Yong-jun, Zhao ; Feng, Wang

Volume

1

fYear

2008

fDate

19-20 Dec. 2008

Firstpage

49

Lastpage

53

Abstract

In this paper a novel method for subspace decomposition and its dimension estimation based on principle components analysis (PCA) neural network is proposed. This method use an improved Sanger PCA network model which can directly process the array data to obtain its signal subspace and does not involve any estimation of the covariance matrix or its Eigen decomposition. Meanwhile, this method can estimate its dimension with the network outputs by AIC criterion. Computer simulation results demonstrate its effectiveness.

Keywords

array signal processing; estimation theory; neural nets; principal component analysis; Sanger principle components analysis; array data processing; array signal processing; dimension estimation; neural network; signal subspace decomposition; Computational intelligence; Computer industry; Conferences; Covariance matrix; Matrix decomposition; Neural networks; Principal component analysis; Recursive estimation; Signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3490-9

Type

conf

DOI

10.1109/PACIIA.2008.109

Filename

4756522