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Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression Selection Consistency
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2016/1/20
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
Canonical correlation analysis DNA copy number Integrative genomic analysis L1 Matrix decomposition Principal component analysis Sparse principal component analysis SVD.
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2015/8/21
We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as X ˆ = k K=1 dkukvk T , where dk, uk, and vk m...
Generalized Canonical Correlation Analysis for Disparate Data Fusion
Manifold matching canonical correlation analysis reduced rank regression efficiency,classification
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2012/11/22
Manifold matching works to identify embeddings of multiple disparate data spaces into the same low-dimensional space, where joint inference can be pursued. It is an enabling methodology for fusion and...
Robust nonlinear canonical correlation analysis:application to seasonal climate forecasting
Robust nonlinear canonical correlation analysis seasonal climate
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2009/10/30
Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasona...