A Rate-Distortion One-Class Model and its Applications to Clustering

author: Partha Pratim Talukdar, Indian Institute of Science Bangalore
published: Aug. 5, 2008,   recorded: July 2008,   views: 5848


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We study the problem of one-class classification, in which we seek a rule to separate a coherent subset of instances similar to a few positive examples from a large pool of instances. We find that the problem can be formulated naturally in terms of a rate-distortion tradeoff, which can be analyzed precisely and leads to an efficient algorithm that competes well with two previous one-class methods. We also show that our model can be extended naturally to clustering problems in which it is important to remove background clutter to improve cluster purity.

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