Abstract
We present in this paper a new approach for unsupervised feature selection for non Gaussian data controlled by a finite mixture of generalized Dirichlet distributions. We model each feature by a mixture of two Beta distributions: one relevant and depends on component labels while the second distribution is uninformative for the clustering. The relevance of each feature is then quantified by the mixture weight associated to the relevant Beta distribution. Experiments in summarizing image collections have shown the merits of our approach.
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Boutemedjet, S., Bouguila, N., Ziou, D. (2007). Unsupervised Feature and Model Selection for Generalized Dirichlet Mixture Models. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_30
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DOI: https://doi.org/10.1007/978-3-540-74260-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74258-6
Online ISBN: 978-3-540-74260-9
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