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| On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers简 | |
| 作者姓名： | Tapio Pahikkala Antti Airola Fabian Gieseke and Oliver Kramer |
| 基金项目： | Tapio Pahikkala is supported by the Academy of Finland under Grant No. 134020 and Fabian Gieseke by the German Academic Exchange Service （DAAD）. |
| 摘 要： | In this work we present the first efficient algorithm for unsupervised training of multi-class regularized least- squares classifiers. The approach is closely related to the unsupervised extension of the support vector machine classifier known as maximum margin clustering, which recently has received considerable attention, though mostly considering the binary classification case. We present a combinatorial search scheme that combines steepest descent strategies with powerful meta-heuristics for avoiding bad local optima. The regularized least-squares based formulation of the problem allows us to use matrix algebraic optimization enabling constant time checks for the intermediate candidate solutions during the search. 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 datasets. Both time complexity analysis and experimental comparisons show that the method can scale well to practical sized problems. |
| 关 键 词： | unsupervised learning multi-class regularized least-squares classification maximum margin clustering corn-binatorial optimization |
On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers |
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| Tapio Pahikkala,Antti Airola,Fabian Gieseke,and Oliver Kramer.On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers[J].Journal of Computer Science and Technology,2014,29(1):90-104. | |
| Authors: | Tapio Pahikkala Antti Airola Fabian Gieseke Oliver Kramer |
| Affiliation: | 1. Department of Information Technology, University of Turku, Turku, 20520, Finland 2. Department of Computer Science, University of Copenhagen, Copenhagen, K 1017, Denmark 3. Computer Science Department, Carl von Ossietzky University of Oldenburg, Oldenburg, 26111, Germany |
| Abstract: | In this work we present the first efficient algorithm for unsupervised training of multi-class regularized least-squares classifiers. The approach is closely related to the unsupervised extension of the support vector machine classifier known as maximum margin clustering, which recently has received considerable attention, though mostly considering the binary classification case. We present a combinatorial search scheme that combines steepest descent strategies with powerful meta-heuristics for avoiding bad local optima. The regularized least-squares based formulation of the problem allows us to use matrix algebraic optimization enabling constant time checks for the intermediate candidate solutions during the search. 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 datasets. Both time complexity analysis and experimental comparisons show that the method can scale well to practical sized problems. |
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