Boosting for Model-Based Data Clustering

In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn the base clustering models. The experimental results on relatively large scale datasets and also Caltech4 object recognition set demonstrate how the performance of relatively simple and computationally efficient base clustering algorithms could be boosted using the proposed algorithm. Key words: Boosting, Model-Based Clustering, Ensemble Methods.

• Type: Conference
• Year: 2008
• Where: DAGM
• Authors: Amir Saffari, Horst Bischof
• Keywords: Base Clustering Models, DAGM 2008, Image Processing, Model-based Clustering, Model-based Data Clustering