Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
Bo Dai (NLPR/LIAMA) and Baogang Hu (NLPR/LIAMA);
JMLR W&P 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 Renyifs 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.