A rate-distortion one-class model and its applications to clustering
Koby Crammer, Partha Talukdar, Fernando M. B. Pereira · 2008
In one-class classification we seek a rule to find a coherent subset of instances similar to a few positive examples in a large pool of instances. The problem can be formulated and analyzed naturally in a rate-distortion framework, leading to an efficient algorithm that compares well with two previous one-class methods. The model can be also be extended to remove background clutter in clustering to improve cluster purity.