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A Rate-Distortion One-Class Model and its Applications to Clustering
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
