Unsupervised multi-class regularized least-squares classification

Tapio Pahikkala*, Antti Airola, Fabian Gieseke, Oliver Kramer

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

8 Citationer (Scopus)

Abstract

Regularized least-squares classification is one of the most promising alternatives to standard support vector machines, with the desirable property of closed-form solutions that can be obtained analytically, and efficiently. While the supervised, and mostly binary case has received tremendous attention in recent years, unsupervised multi-class settings have not yet been considered. In this work we present an efficient implementation for the unsupervised extension of the multi-class regularized least-squares classification framework, which is, to the best of the authors' knowledge, the first one in the literature addressing this task. The resulting kernel-based framework efficiently combines steepest descent strategies with powerful meta-heuristics for avoiding local minima. The computational efficiency of the overall approach is ensured through the application of matrix algebra shortcuts that render efficient updates of the intermediate candidate solutions possible. Our experimental evaluation indicates the potential of the novel method, and demonstrates its superior clustering performance over a variety of competing methods on real-world data sets.

OriginalsprogEngelsk
TitelProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Antal sider10
ForlagIEEE
Publikationsdato2012
Sider585-594
Artikelnummer6413868
ISBN (Trykt)978-1-4673-4649-8
DOI
StatusUdgivet - 2012
Udgivet eksterntJa
Begivenhed12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgien
Varighed: 10 dec. 201213 dec. 2012

Konference

Konference12th IEEE International Conference on Data Mining, ICDM 2012
Land/OmrådeBelgien
ByBrussels
Periode10/12/201213/12/2012

Citationsformater