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TCA: High Dimensional Principal Component Analysis for non-Gaussian Data
Fang Han
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Oznake: computer science;machine learning;semi-supervised learning;statistical learning;unsupervised learning
We propose a high dimensional semiparametric scaleinvariant principle component analysis, named TCA, by utilize the natural connection between the elliptical distribution family and the principal component analysis. Elliptical distribution family includes many well-known multivariate distributions l ...
Leto: 2012 Vir: videolectures.net
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
Fang Han
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Oznake: computer science;machine learning
In this paper we focus on the principal component regression and its application to high dimension non-Gaussian data. The major contributions are in two folds. First, in low dimensions and under a double asymptotic framework where both the dimension d and sample size n can increase, by borrowing the ...
Leto: 2014 Vir: videolectures.net
Št. zadetkov: 2
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