Unsupervised multi-class regularized least-squares classification
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Unsupervised multi-class regularized least-squares classification. / Pahikkala, Tapio; Airola, Antti; Gieseke, Fabian; Kramer, Oliver.
Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012. IEEE, 2012. p. 585-594 6413868.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Unsupervised multi-class regularized least-squares classification
AU - Pahikkala, Tapio
AU - Airola, Antti
AU - Gieseke, Fabian
AU - Kramer, Oliver
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Maximum margin clustering
KW - Multi-class regularized least-squares classification
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=84874052459&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2012.71
DO - 10.1109/ICDM.2012.71
M3 - Article in proceedings
AN - SCOPUS:84874052459
SN - 978-1-4673-4649-8
SP - 585
EP - 594
BT - Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
PB - IEEE
T2 - 12th IEEE International Conference on Data Mining, ICDM 2012
Y2 - 10 December 2012 through 13 December 2012
ER -
ID: 167918602