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Pavlidis, NG, Hofmeyr, DP and Tasoulis, SK (2016) Minimum Density Hyperplanes. Journal of Machine Learning Research, 17 (156). pp. 1-33. ISSN 1532-4435
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Abstract
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classification which is motivated by this objective. The proposed minimum density hyperplane minimises the integral of the empirical probability density function along it, thereby avoiding intersection with high density clusters. We show that the minimum density and the maximum margin hyperplanes are asymptotically equivalent, thus linking this approach to maximum margin clustering and semi-supervised support vector classifiers. We propose a projection pursuit formulation of the associated optimisation problem which allows us to find minimum density hyperplanes efficiently in practice, and evaluate its performance on a range of benchmark datasets. The proposed approach is found to be very competitive with state of the art methods for clustering and semi-supervised classification.
| Item Type: | Article |
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| Uncontrolled Keywords: | 08 Information And Computing Sciences, 17 Psychology And Cognitive Sciences |
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | Journal of Machine Learning Research |
| Related URLs: | |
| Date Deposited: | 03 Feb 2017 09:43 |
| Last Modified: | 04 Sep 2021 11:59 |
| DOI or ID number: | 17(156): 1-33, 2016 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/5422 |
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