贝叶斯风险加权向量量化

Proceedings of 1994 Workshop on Information Theory and Statistics Pub Date : 1994-10-27 DOI:10.1109/WITS.1994.513847

R. Gray

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引用次数: 8

摘要

有损压缩和分类算法都试图将大量可能的观察结果减少到几个有代表性的类别，以保留基本信息。描述了一个将分类和压缩组合成一个或两个量化器的框架以及一些示例，并与其他基于量化器的分类方案相关。

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Bayes risk-weighted vector quantization

Lossy compression and classification algorithms both attempt to reduce a large collection of possible observations into a few representative categories so as to preserve essential information. A framework for combining classification and compression into one or two quantizers is described along with some examples and related to other quantizer-based classification schemes.

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Proceedings of 1994 Workshop on Information Theory and Statistics

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