Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
Bo Dai, Bao-Gang Hu · 2010
In this paper, we introduce an assumption which makes it possible to extend the learn-ing 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 projec-tions 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.