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 languageEnglish (US)
Title of host publicationGraph Embedding for Pattern Analysis
PublisherSpringer New York
Number of pages17
ISBN (Print)9781461444572, 9781461444565
StatePublished - Jan 1 2013


ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yang, S., Yuan, L., Lai, Y-C., Shen, X., Wonka, P., & Ye, J. (2013). Feature grouping and selection over an undirected graph. In Graph Embedding for Pattern Analysis (pp. 27-43). Springer New York. https://doi.org/10.1007/978-1-4614-4457-2_2