We propose in this paper a new fully unsupervised model based on a Dirichlet process prior and the inverted Dirichlet distribution that allows the automatic inferring of clusters from data. The main idea is to let the number of mixture components increases as new vectors arrive. This allows answering the model selection problem in a elegant way since the resulting model can be viewed as an infinite inverted Dirichlet mixture. An expectation propagation (EP) inference methodology is developed to learn this model by obtaining a full posterior distribution on its parameters. We validate the model on a challenging application namely image spam filtering to show the merits of the framework.
CITATION STYLE
Fan, W., Bourouis, S., Bouguila, N., Aldosari, F., Sallay, H., & Khayyat, K. M. J. (2018). EP-based infinite inverted dirichlet mixture learning: Application to image spam detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10868 LNAI, pp. 342–354). Springer Verlag. https://doi.org/10.1007/978-3-319-92058-0_33
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