# Vector Quantization by Minimizing Kullback-Leibler Divergence

Source: [Vector Quantization by Minimizing Kullback-Leibler Divergence](<https://www.sota2.com/research/paper/68fa61b41e209131ce9f58dc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:04:28.118Z

Authors: Lan Yang, Jingbin Wang, Yujin Tu, Prarthana Mahapatra, Nelson Cardoso

Published: 2015-01-30T07:16:50.000Z

## Abstract

This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set. In this way, the vector quantization output can keep as much information of the class label as possible. An objective function is constructed and we also developed an iterative algorithm to minimize it. The new method is evaluated on bag-of-features based image classification problem.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1501.07681>)

[PDF](<https://arxiv.org/pdf/1501.07681.pdf>)