A kernel approach for vector quantization with guaranteed distortion bounds
Tipping, M. E. and B. Schölkopf
We 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.