Unsupervised and Semi-Supervised Multi-Class Support Vector Machines
We present new unsupervised and semi-supervised training
algorithms for multi-class support vector machines based on
semidefinite programming. Although support vector machines
(SVMs) have been a dominant machine learning technique
for the past decade, they have generally been applied
to supervised learning problems. Developing unsupervised
extensions to SVMs has in fact proved to be difficult. In
this paper, we present a principled approach to unsupervised
SVM training by formulating convex relaxations of the natural
training criterion: find a labeling that would yield an optimal
SVM classifier on the resulting training data. The problem
is hard, but semidefinite relaxations can approximate this
objective surprisingly well. While previous work has concentrated
on the two-class case, we present a general, multi-class
formulation that can be applied to a wider range of natural
data sets. The resulting training procedures are computationally
intensive, but produce high quality generalization results.
Citation
L. Xu,
D. Schuurmans.
"Unsupervised and Semi-Supervised Multi-Class Support Vector Machines".
National Conference on Artificial Intelligence (AAAI), Pittsburgh, January 2005.
Keywords: |
vector, multi-class, machine learning |
Category: |
In Conference |
BibTeX
@incollection{Xu+Schuurmans:AAAI05,
author = {Linli Xu and Dale Schuurmans},
title = {Unsupervised and Semi-Supervised Multi-Class Support Vector
Machines},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2005,
}
Last Updated: April 25, 2007
Submitted by William Thorne