A new scatter-based multi-class support vector machine

Robert Jenssen, Marius Kloft, Sören Sonnenburg, Alexander Zien, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a fast chunking-based optimizer. Promising results are reported, also compared to the state-of-the-art, at lower computational complexity.

Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing
DOIs
Publication statusPublished - 2011 Dec 5
Externally publishedYes
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: 2011 Sep 182011 Sep 21

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period11/9/1811/9/21

Fingerprint

Support vector machines
Computational complexity

Keywords

  • μ-SVM
  • multi-class
  • scatter

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Cite this

Jenssen, R., Kloft, M., Sonnenburg, S., Zien, A., & Muller, K. (2011). A new scatter-based multi-class support vector machine. In IEEE International Workshop on Machine Learning for Signal Processing [6064625] https://doi.org/10.1109/MLSP.2011.6064625

A new scatter-based multi-class support vector machine. / Jenssen, Robert; Kloft, Marius; Sonnenburg, Sören; Zien, Alexander; Muller, Klaus.

IEEE International Workshop on Machine Learning for Signal Processing. 2011. 6064625.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jenssen, R, Kloft, M, Sonnenburg, S, Zien, A & Muller, K 2011, A new scatter-based multi-class support vector machine. in IEEE International Workshop on Machine Learning for Signal Processing., 6064625, 21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011, Beijing, China, 11/9/18. https://doi.org/10.1109/MLSP.2011.6064625
Jenssen R, Kloft M, Sonnenburg S, Zien A, Muller K. A new scatter-based multi-class support vector machine. In IEEE International Workshop on Machine Learning for Signal Processing. 2011. 6064625 https://doi.org/10.1109/MLSP.2011.6064625
Jenssen, Robert ; Kloft, Marius ; Sonnenburg, Sören ; Zien, Alexander ; Muller, Klaus. / A new scatter-based multi-class support vector machine. IEEE International Workshop on Machine Learning for Signal Processing. 2011.
@inproceedings{9822bdd217d348b59c9444a306c06572,
title = "A new scatter-based multi-class support vector machine",
abstract = "We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a fast chunking-based optimizer. Promising results are reported, also compared to the state-of-the-art, at lower computational complexity.",
keywords = "μ-SVM, multi-class, scatter",
author = "Robert Jenssen and Marius Kloft and S{\"o}ren Sonnenburg and Alexander Zien and Klaus Muller",
year = "2011",
month = "12",
day = "5",
doi = "10.1109/MLSP.2011.6064625",
language = "English",
isbn = "9781457716232",
booktitle = "IEEE International Workshop on Machine Learning for Signal Processing",

}

TY - GEN

T1 - A new scatter-based multi-class support vector machine

AU - Jenssen, Robert

AU - Kloft, Marius

AU - Sonnenburg, Sören

AU - Zien, Alexander

AU - Muller, Klaus

PY - 2011/12/5

Y1 - 2011/12/5

N2 - We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a fast chunking-based optimizer. Promising results are reported, also compared to the state-of-the-art, at lower computational complexity.

AB - We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a fast chunking-based optimizer. Promising results are reported, also compared to the state-of-the-art, at lower computational complexity.

KW - μ-SVM

KW - multi-class

KW - scatter

UR - http://www.scopus.com/inward/record.url?scp=82455212637&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=82455212637&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2011.6064625

DO - 10.1109/MLSP.2011.6064625

M3 - Conference contribution

AN - SCOPUS:82455212637

SN - 9781457716232

BT - IEEE International Workshop on Machine Learning for Signal Processing

ER -