A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:298-303, 2001.
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.