Neural Network Implementations for PCA and Its Extensions
Joint Authors
Du, K.-L.
Lu, Jiabin
Zhang, Biaobiao
Wang, Hui
Qiu, Jialin
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-19
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
Many information processing problems can be transformed into some form of eigenvalue or singular value problems.
Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems.
In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions.
PCA is a statistical method that is directly related to EVD and SVD.
Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems.
The algorithms are typical unsupervised learning methods.
Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described.
Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion.
These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression.
American Psychological Association (APA)
Qiu, Jialin& Wang, Hui& Lu, Jiabin& Zhang, Biaobiao& Du, K.-L.. 2012. Neural Network Implementations for PCA and Its Extensions. ISRN Artificial Intelligence،Vol. 2012, no. 2012, pp.1-19.
https://search.emarefa.net/detail/BIM-502956
Modern Language Association (MLA)
Qiu, Jialin…[et al.]. Neural Network Implementations for PCA and Its Extensions. ISRN Artificial Intelligence No. 2012 (2012), pp.1-19.
https://search.emarefa.net/detail/BIM-502956
American Medical Association (AMA)
Qiu, Jialin& Wang, Hui& Lu, Jiabin& Zhang, Biaobiao& Du, K.-L.. Neural Network Implementations for PCA and Its Extensions. ISRN Artificial Intelligence. 2012. Vol. 2012, no. 2012, pp.1-19.
https://search.emarefa.net/detail/BIM-502956
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-502956