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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Popis
Shrnutí: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 (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.
Bibliografie:ObjectType-Article-1
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ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2006.09.011