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.
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Smeraldi, F., Bicego, M., Cristani, M., Murino, V. (2011). CLOOSTING: CLustering Data with bOOSTING. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_31
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DOI: https://doi.org/10.1007/978-3-642-21557-5_31
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