EP-Based Infinite Inverted Dirichlet Mixture Learning: Application to Image Spam Detection
| Autor: | Sami Bourouis, K. M. Jamil Khayyat, Fahd M. Aldosari, Wentao Fan, Nizar Bouguila, Hassen Sallay |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Model selection Posterior probability Inference 02 engineering and technology 01 natural sciences Image spam Dirichlet distribution Dirichlet process 010104 statistics & probability symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Expectation propagation 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing 0101 mathematics Algorithm |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319920573 IEA/AIE |
| DOI: | 10.1007/978-3-319-92058-0_33 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|