Regularization for High Dimensional Data
Yongdai Kim, Seoul National University

Various regularization methods for analyzing high dimensional data and their applications to image data analysis, bioinformatics and covariance estimation are reviewed. Various penalties including Lasso, Dantzig, SCAD, fused Lasso used for regularized regression are introduced and compared. Also, some unsolved problems related to regularization methods are discussed.

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