Cloosting: clustering data with boosting

Authors:
F. Smeraldi;M. Bicego;M. Cristani;V. Murino
Affiliations:
School of Electronic Engineering and Computer Science, Queen Mary University of London, UK;Computer Science Department, University of Verona, Italy and Istituto Italiano di Tecnologia, Italy;Computer Science Department, University of Verona, Italy and Istituto Italiano di Tecnologia, Italy;Computer Science Department, University of Verona, Italy and Istituto Italiano di Tecnologia, Italy
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

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Abstract

We present a novel clustering approach, that exploits boosting as the primary means of modelling clusters. Typically, boosting is applied in a supervised classification context; here, we move in the less explored unsupervised scenario. Starting from an initial partition, clusters are iteratively re-estimated using the responses of one-vs-all boosted classifiers. Within-cluster homogeneity and separation between the clusters are obtained by a combination of three mechanisms: use of regularised Adaboost to reject outliers, use of weak learners inspired to subtractive clustering and smoothing of the decision functions with a Gaussian Kernel. Experiments on public datasets validate our proposal, in some cases improving on the state of the art.