| Creators: |
Faußer, Stefan A. and Schwenker, Friedhelm |
| Title: |
Semi-Supervised Kernel Clustering with Sample-to-cluster Weights |
| Item Type: |
Conference or Workshop Item |
| Event Title: |
(Proceedings of the) 1st IAPR TC3 Workshop, PSL 2011 |
| Event Location: |
Ulm, Germany |
| Event Dates: |
15.-16. September 2011 |
| Page Range: |
pp. 72-81 |
| Date: |
2012 |
| Divisions: |
Informationsmanagement |
| Abstract (ENG): |
Collecting unlabelled data is often effortless while labelling them can be difficult. Either the amount of data is too large or samples cannot be assigned a specific class label with certainty. In semi-supervised clustering the aim is to set the cluster centres close to their label-matching samples and unlabelled samples. Kernel based clustering methods are known to improve the cluster results by clustering in feature space. In this paper we propose a semi-supervised kernel based clustering algorithm that minimizes convergently an error function with sample-to-cluster weights. These sample-to-cluster weights are set dependent on the class label, i.e. matching, not-matching or unlabelled. The algorithm is able to use many kernel based clustering methods although we suggest Kernel Fuzzy C-Means, Relational Neural Gas and Kernel K-Means. We evaluate empirically the performance of this algorithm on two real-life dataset, namely Steel Plates Faults and MiniBooNE. |
| Forthcoming: |
No |
| Language: |
English |
| Citation: |
Faußer, Stefan A. and Schwenker, Friedhelm
(2012)
Semi-Supervised Kernel Clustering with Sample-to-cluster Weights.
In: (Proceedings of the) 1st IAPR TC3 Workshop, PSL 2011, 15.-16. September 2011, Ulm, Germany, pp. 72-81.
ISBN 9783642282577
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