Fast principal component extraction by a homogeneous neural network

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 2001, Pages 1273-1276

  Ouyang, Shan ^a   Bao, Zheng ^b  

^a GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

^b XIDIAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

PRINCIPAL COMPONENT EXTRACTION; RECURSIVE LEAST SQUARES VERSION; WEIGHTED INFORMATION CRITERION;

NEURAL NETWORKS;

EID: 0034843052     PISSN: 15206149     EISSN: None     Source Type: Journal    
DOI: 10.1109/ICASSP.2001.941157     Document Type: Article

References (13)

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4. Projection approximation subspace tracking
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6. Least mean square error reconstruction principal for self-organizing neural-nets
7. Principal component analysis by homogeneous neural networks, part I: Weighted subspace criterion
8. Principal component analysis by homogeneous neural networks, part II: Analysis and extensions of the learning algorithms
9. Fast subspace tracking and neural network learning by a novel information criterion
10. Principal components, minor components, and linear neural networks
11. On relative convergence properties of principal component analysis algorithms