Abstract
High-dimensional regression/classification is challenging due to the curse of dimensionality. Lasso [18] and its various extensions [10], which can simultaneously perform feature selection and regression/classification, have received increasing attention in this situation. However, in the presence of highly correlated features lasso tends to only select one of those features resulting in suboptimal performance [25]. Several methods have been proposed to address this issue in the literature. Shen and Ye [15] introduce an adaptive model selection procedure that corrects the estimation bias through a data-driven penalty based on generalized degrees of freedom.
Original language | English (US) |
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Title of host publication | Graph Embedding for Pattern Analysis |
Publisher | Springer New York |
Pages | 27-43 |
Number of pages | 17 |
ISBN (Electronic) | 9781461444572 |
ISBN (Print) | 9781461444565 |
DOIs | |
State | Published - Jan 1 2013 |
ASJC Scopus subject areas
- General Engineering