A learning algorithm for adaptive canonical correlation analysis of several data sets

Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVA...

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Vydáno v:	Neural networks Ročník 20; číslo 1; s. 139 - 152
Hlavní autoři:	VIA, Javier, SANTAMARIA, Ignacio, PEREZ, Jesus
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 2007 Elsevier Science
Témata:	Adaptive algorithms Algorithms Applied sciences Artificial intelligence Canonical correlation analysis (CCA) Computer science; control theory; systems Connectionism. Neural networks Exact sciences and technology Hebbian learning Humans Learning Learning and adaptive systems Multivariate Analysis Neural Networks (Computer) Principal component analysis (PCA) Recursive least squares (RLS) Hebbian learning Recursive least squares (RLS) Adaptive algorithms Canonical correlation analysis (CCA) Principal component analysis (PCA) Adaptive algorithm Two layer model Projection Canonical correlation Variance Canonical analysis Feedforward neural nets Least squares method Work Learning algorithm Tool Data analysis Statistical analysis Eigenvector Least squares problem Neural network Recursive algorithm Statistical method Correlation analysis Recursive method Artificial intelligence
ISSN:	0893-6080, 1879-2782
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