DocumentCode
1567344
Title
Convergence Analysis of an Effective MCA Learning Algorithm
Author
Peng, Dezhong ; Yi, Zhang
Author_Institution
Sch. of Comput. Sci. & Eng., UESTC, Chengdu
Volume
3
fYear
2005
Firstpage
2003
Lastpage
2008
Abstract
Minor component analysis (MCA) has many important applications in signal processing and data analysis. Convergence is essential for MCA algorithms towards practical applications. This paper reviews an effective MCA algorithm and analyzes the convergence of this algorithm via deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee the convergence of this learning algorithm. Simulations are carried out to further illustrate the theoretical results achieved
Keywords
convergence; discrete time systems; learning (artificial intelligence); statistical analysis; MCA learning algorithm; convergence analysis; deterministic discrete time method; minor component analysis; Algorithm design and analysis; Application software; Computational intelligence; Computer science; Convergence; Data mining; Discrete cosine transforms; Laboratories; Principal component analysis; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1615017
Filename
1615017
Link To Document