Vector-Quantization by density matching in the minimum Kullback-Leibler divergence sense

Anant Hegde, Deniz Erdogmus, Tue Lehn-Schiøler, Yadunandana Rao, Jose Principe

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    359 Downloads (Pure)


    Representation of a large set of high-dimensional data is a fundamental problem in many applications such as communications and biomedical systems. The problem has been tackled by encoding the data with a compact set of code-vectors called processing elements. In this study, we propose a vector quantization technique that encodes the information in the data using concepts derived from information theoretic learning. The algorithm minimizes a cost function based on the Kullback-Liebler divergence to match the distribution of the processing elements with the distribution of the data. The performance of this algorithm is demonstrated on synthetic data as well as on an edge-image of a face. Comparisons are provided with some of the existing algorithms such as LBG and SOM.
    Original languageEnglish
    Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
    Publication date2004
    Publication statusPublished - 2004


    Dive into the research topics of 'Vector-Quantization by density matching in the minimum Kullback-Leibler divergence sense'. Together they form a unique fingerprint.

    Cite this