Abstract
Variational learning of mixture models has proved to be effective in recent research. In this paper, we propose a generalized inverted Dirichlet based mixture model with an incremental variational algorithm. We incorporate feature selection and a component splitting approach for model selection within the variational framework. This helps us estimate the complexity of the data efficiently concomitantly eliminating the irrelevant features. We validate our model with two challenging applications; image categorization and dynamic texture categorization.
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Acknowledgement
The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC) and Concordia University Research Chair Tier 2.
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Maanicshah, K., Ali, S., Fan, W., Bouguila, N. (2019). Unsupervised Variational Learning of Finite Generalized Inverted Dirichlet Mixture Models with Feature Selection and Component Splitting. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_8
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