Sparse boosting

Zhen James Xiang; Peter Jeffrey Ramadge

doi:10.1109/ICASSP.2009.4959911

Sparse boosting

Zhen James Xiang, Peter Jeffrey Ramadge

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l₁-regularized Adaboost exponential loss function.

Original language	English (US)
Title of host publication	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages	1625-1628
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2009.4959911
State	Published - 2009
Event	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China Duration: Apr 19 2009 → Apr 24 2009

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/Territory	Taiwan, Province of China
City	Taipei
Period	4/19/09 → 4/24/09

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

Algorithms
Optimization methods
Pattern classification
Signal representations

Access to Document

10.1109/ICASSP.2009.4959911

Cite this

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title = "Sparse boosting",

abstract = "We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized Adaboost exponential loss function.",

keywords = "Algorithms, Optimization methods, Pattern classification, Signal representations",

author = "Xiang, {Zhen James} and Ramadge, {Peter Jeffrey}",

year = "2009",

doi = "10.1109/ICASSP.2009.4959911",

language = "English (US)",

isbn = "9781424423545",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "1625--1628",

booktitle = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009",

note = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 ; Conference date: 19-04-2009 Through 24-04-2009",

}

Xiang, ZJ & Ramadge, PJ 2009, Sparse boosting. in 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009., 4959911, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1625-1628, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, Taiwan, Province of China, 4/19/09. https://doi.org/10.1109/ICASSP.2009.4959911

Sparse boosting. / Xiang, Zhen James; Ramadge, Peter Jeffrey.
2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009. 2009. p. 1625-1628 4959911 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Xiang, Zhen James

AU - Ramadge, Peter Jeffrey

PY - 2009

Y1 - 2009

N2 - We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized Adaboost exponential loss function.

AB - We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost. The algorithm can be viewed as a coordinate descent method for the l1-regularized Adaboost exponential loss function.

KW - Algorithms

KW - Optimization methods

KW - Pattern classification

KW - Signal representations

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DO - 10.1109/ICASSP.2009.4959911

M3 - Conference contribution

AN - SCOPUS:70349220929

SN - 9781424423545

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 1625

EP - 1628

BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009

T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009

Y2 - 19 April 2009 through 24 April 2009

ER -

Sparse boosting

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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