A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds
Abstract
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.
Cite
Text
Tipping and Schölkopf. "A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Tipping and Schölkopf. "A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/tipping2001aistats-kernel/)BibTeX
@inproceedings{tipping2001aistats-kernel,
title = {{A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds}},
author = {Tipping, Michael E. and Schölkopf, Bernhard},
booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
year = {2001},
pages = {298-303},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/tipping2001aistats-kernel/}
}