Robust dimension reduction based on canonical correlation
نویسندگان
چکیده
منابع مشابه
Dimension Reduction Based on Canonical Correlation
Dimension reduction is helpful and often necessary in exploring nonlinear or nonparametric regression structures with a large number of predictors. We consider using the canonical variables from the design space whose correlations with a spline basis in the response space are significant. The method can be viewed as a variant of sliced inverse regression (SIR) with simple slicing replaced by Bs...
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Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are differen...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2009
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2008.04.003