Name
Title | ||
|---|---|---|
Variational learning of a shifted scaled Dirichlet model with component splitting approach |
Abstract | ||
|---|---|---|
Mixture models have become arguably one of the most widely used statistical approaches to perform inference on various types of data and have been successfully applied in data mining and numerous real world applications. In this work, we focus on the variational learning of finite shifted scaled Dirichlet mixture models. Component splitting is one of the major assets of our model which prevents over-fitting. Furthermore, the number of components can be estimated automatically and simultaneously along with parameters estimation. The performance and effectiveness of proposed model is verified by experimenting on two real-life applications, namely, occupancy estimation and activities recognition in smart buildings. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/AI4I49448.2020.00024 | 2020 Third International Conference on Artificial Intelligence for Industries (AI4I) |
Keywords | DocType | ISBN |
Mixture models,variational inference,component splitting,Shifted scaled Dirichlet distribution,smart buildings | Conference | 978-1-7281-8702-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narges Manouchehri | 1 | 1 | 2.72 |
| Oumayma Dalhoumi | 2 | 0 | 0.34 |
| Manar Amayri | 3 | 0 | 4.39 |
| Nizar Bouguila | 4 | 1539 | 146.09 |