Abstract
In this paper, we develop the predictive distribution of the generalized inverted Dirichlet (GID) mixture model using local variational inference. The main goal is to be able to tackle classification problems involving small training data sets. The two main ingredients of the proposed predictive model are the GID distribution which provides flexibility for the modeling of semi-bounded data that are naturally generated by different sensors outputs and the efficient of variational inference as a deterministic approximation to fully Bayesian approaches. The merits of the proposed model are shown via synthetic data and a real application that concerns activities recognition.
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Acknowledgement
The completion of this research was made possible thanks to Natural Sciences and Engineering Research Council of Canada (NSERC), the “Nouveaux arrivants Université Grenoble Alpes, Grenoble INP - UGA, G-SCOP" program and the National Natural Science Foundation of China (61876068).
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Guo, J., Amayri, M., Fan, W., Bouguila, N. (2022). A Generalized Inverted Dirichlet Predictive Model for Activity Recognition Using Small Training Data. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_36
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DOI: https://doi.org/10.1007/978-3-031-08530-7_36
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