DocumentCode
31220
Title
Online Homotopy Algorithm for a Generalization of the LASSO
Author
Hofleitner, A. ; Rabbani, T. ; El Ghaoui, Laurent ; Bayen, A.
Author_Institution
Electr. Eng. & Comput. Sci., UC Berkeley, Berkeley, CA, USA
Volume
58
Issue
12
fYear
2013
fDate
Dec. 2013
Firstpage
3175
Lastpage
3179
Abstract
The LASSO is a widely used shrinkage method for linear regression. We propose an online homotopy algorithm to solve a generalization of the LASSO in which the l1 regularization is applied on a linear transformation of the solution, allowing to input prior information on the structure of the problem and to improve interpretability of the results. The algorithm takes advantage of the sparsity of the solution for computational efficiency and is promising for mining large datasets.
Keywords
data mining; learning (artificial intelligence); regression analysis; LASSO; computational efficiency; interpretability; large dataset mining; linear regression; linear transformation; online homotopy algorithm; shrinkage method; Estimation; Optimization; Polynomials; Probes; Signal processing algorithms; Vehicles; LASSO;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2013.2259373
Filename
6506951
Link To Document