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titleA kernel approach for vector quantization with guaranteed distortion bounds
authorsTipping, M. E. and B. Schölkopf
abstractWe propose a kernel method for vector quantization and clustering. Our approach allows a priori specification of the maximally allowed distortion, and it automatically finds a sufficient representative subset of the data to act as codebook vectors (or cluster centres). It does not find the minimal number of such vectors, which would amount to a combinatorial problem; however, we find a `good' quantization through linear programming.
typeConference Paper
journalArtificial Intelligence and Statistics 2001
published year
(Total records:1429)
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