Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering


Bo Dai, Baogang Hu ;
Proceedings of 2nd Asian Conference on Machine Learning, PMLR 13:47-62, 2010.


In this paper, we introduce an assumption which makes it possible to extend the learning ability of discriminative model to unsupervised setting. We propose an information-theoretic framework as an implementation of the low-density separation assumption. The proposed framework provides a unified perspective of Maximum Margin Clustering (MMC), Discriminative k-means, Spectral Clustering and Unsupervised Renyi's Entropy Analysis and also leads to a novel and efficient algorithm, Accelerated Maximum Relative Margin Clustering (ARMC), which maximizes the margin while considering the spread of projections and affine invariance. Experimental results show that the proposed discriminative unsupervised learning method is more efficient in utilizing data and achieves the state-of-the-art or even better performance compared with mainstream clustering methods.

Related Material