Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Home
Search
Quick Search
Advanced
Fulltext
Browse
Collections
Persons
My eDoc
Session History
Login
Name:
Password:
Documentation
Help
Support Wiki
Direct access to
document ID:


          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548309.0, MPI für biologische Kybernetik / Biologische Kybernetik
A scalable trust-region algorithm with application to mixed-norm regression
Authors:Kim, D.; Sra, S.; Dhillon, I.
Editors:Fürnkranz, J.; Joachims, T.
Date of Publication (YYYY-MM-DD):2010-06
Title of Proceedings:Proceedings of the 27th International Conference on Machine Learning (ICML 2010)
Start Page:519
End Page:526
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably, for sparsity-promoting regularizers such as l_1 or l_{1,infty} norms it enables efficient computation of sparse solutions. Our approach is based on the trust-region framework with nonsmooth objectives, which allows us to build on known results to provide convergence analysis. We avoid the computational overheads associated with the conventional Hessian approximation used by trust-region methods by instead using a simple separable quadratic approximation. This approximation also enables use of proximity operators for tackling nonsmooth regularizers. We illustrate the versatility of our resulting algorithm by specializing it to three mixed-norm regression problems: group lasso [36], group logistic regression [21], and multi-task lasso [19]. We experiment with both synthetic and real-world large-scale data—our method is seen to be com
petitive, robust, and scalable.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6519
URL:http://www.icml2010.org/
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.