Title :
A principal component network for generalized eigen-decomposition
Author :
Xu, Dongxin ; Principe, Jose C. ; Wu, Hsiao-Chun
Author_Institution :
Comput. Neuroeng. Lab., Florida Univ., Gainesville, FL, USA
Abstract :
This paper presents a novel principal component network with simple online local learning rules to obtain generalized eigenvalues and their corresponding eigenvectors of input data. The network is composed of a set of forward linear projections and a set of linear lateral inhibition connections between them. The rules for both forward and lateral connections are not only mathematically rooted but also local which confers them biological plausibility. Stationary points of the rules and their stability are analyzed. Simulations are given to verify, the validity and effectiveness of the proposed method
Keywords :
circuit stability; eigenvalues and eigenfunctions; learning (artificial intelligence); neural nets; real-time systems; signal processing; eigen-decomposition; eigenvalues; eigenvectors; forward connections; forward linear projections; lateral connections; learning rules; online local learning; principal component network; stability; Autocorrelation; Computer networks; Eigenvalues and eigenfunctions; Gradient methods; Laboratories; Linear discriminant analysis; Matrix decomposition; Neural engineering; Principal component analysis; Random processes;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685878
