A new scatter-based multi-class support vector machine

Jenssen, R; Kloft, M; Sonnenburg, S; Zien, A; Müller, K-R

doi:10.1109/MLSP.2011.6064625

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

A new scatter-based multi-class support vector machine

MPS-Authors

/persons/resource/persons84960

Sonnenburg, S
Rätsch Group, Friedrich Miescher Laboratory, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Jenssen, R., Kloft, M., Sonnenburg, S., Zien, A., & Müller, K.-R. (2011). A new scatter-based multi-class support vector machine. In T. Tan (Ed.), 2011 IEEE International Workshop on Machine Learning for Signal Processing. Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/21.11116/0000-0010-5657-C

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.