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 :
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