Sciweavers

Share
AAAI
2012

Discriminative Clustering via Generative Feature Mapping

8 years 5 months ago
Discriminative Clustering via Generative Feature Mapping
Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we explicitly impose the maximum entropy principle. We also propose the unified probabilistic framework, enabling solvers using standard techniques. Experiments on a variety of datasets bear out the notable benefit of our method in terms of adaptiveness and robustness.
Liwei Wang, Xiong Li, Zhuowen Tu, Jiaya Jia
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Liwei Wang, Xiong Li, Zhuowen Tu, Jiaya Jia
Comments (0)
books