Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2013.85
Title: Forward basis selection for pursuing sparse representations over a dictionary
Authors: Yuan, X.-T.
Yan, S. 
Keywords: Gaussian graphical models
Greedy selection
optimization
sparse representation
subspace segmentation
Issue Date: 2013
Source: Yuan, X.-T., Yan, S. (2013). Forward basis selection for pursuing sparse representations over a dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (12) : 3025-3036. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2013.85
Abstract: The forward greedy selection algorithm of Frank and Wolfe has recently been applied with success to coordinate-wise sparse learning problems, characterized by a tradeoff between sparsity and accuracy. In this paper, we generalize this method to the setup of pursuing sparse representations over a prefixed dictionary. Our proposed algorithm iteratively selects an atom from the dictionary and minimizes the objective function over the linear combinations of all the selected atoms. The rate of convergence of this greedy selection procedure is analyzed. Furthermore, we extend the algorithm to the setup of learning nonnegative and convex sparse representation over a dictionary. Applications of the proposed algorithms to sparse precision matrix estimation and low-rank subspace segmentation are investigated with efficiency and effectiveness validated on benchmark datasets. © 2013 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56084
ISSN: 01628828
DOI: 10.1109/TPAMI.2013.85
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

4
checked on Nov 22, 2017

Page view(s)

12
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.