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Online Dictionary Learning using Biogeography-based Optimization for Sparse Representation

by Morteza Kolali Khormuji, Mehdi Sadeghzadeh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 4
Year of Publication: 2014
Authors: Morteza Kolali Khormuji, Mehdi Sadeghzadeh
10.5120/17672-8492

Morteza Kolali Khormuji, Mehdi Sadeghzadeh . Online Dictionary Learning using Biogeography-based Optimization for Sparse Representation. International Journal of Computer Applications. 101, 4 ( September 2014), 1-6. DOI=10.5120/17672-8492

@article{ 10.5120/17672-8492,
author = { Morteza Kolali Khormuji, Mehdi Sadeghzadeh },
title = { Online Dictionary Learning using Biogeography-based Optimization for Sparse Representation },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 4 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number4/17672-8492/ },
doi = { 10.5120/17672-8492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:47.351756+05:30
%A Morteza Kolali Khormuji
%A Mehdi Sadeghzadeh
%T Online Dictionary Learning using Biogeography-based Optimization for Sparse Representation
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 4
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computational visual attention modeling is a topic of increasing importance in machine understanding of images. The model with the `-0 norm as constraint is an NP hard problem. How to find the global optimal solution is a difficult point of this area. For Biogeography-based optimization (BBO) is good at solving NP hard problem, a dictionary learning method based on it is proposed in this paper. Biogeography-based optimization (BBO) algorithm is a new category of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. The samples are first classified randomly for generate original population and residual of approximate the sample class with a rank-1 matrix as habitat suitability index (HSI) is calculated. Then, select better individuals using league matches. After that new individuals are generated from migration operators and mutation and the residual of the representation is used as data samples for training the dictionary for the next layer. The experimental results show the algorithm are effective.

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Index Terms

Computer Science
Information Sciences

Keywords

Biogeography-based optimization Sparse Representation Dictionary Learning

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