摘要翻译：
矢量量化(VQ)是信号处理中的一种有损数据压缩技术，它仅限于特征向量，因此不适用于组合结构。这一贡献提供了图量化(GQ)的理论基础，将矢量量化扩展到属性图领域。本文给出了图量化器最优性的Lloyd-Max必要条件，以及基于经验畸变测度和随机优化的最优GQ设计的一致性结果。这些结果在统计上证明了已有的图域聚类算法的正确性。该方法提供了一个模板，说明如何将GQ以外的结构模式识别方法与统计模式识别相结合。
---
英文标题：
《Graph Quantization》
---
作者：
Brijnesh J. Jain and Klaus Obermayer
---
最新提交年份：
2010
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  Vector quantization(VQ) is a lossy data compression technique from signal processing, which is restricted to feature vectors and therefore inapplicable for combinatorial structures. This contribution presents a theoretical foundation of graph quantization (GQ) that extends VQ to the domain of attributed graphs. We present the necessary Lloyd-Max conditions for optimality of a graph quantizer and consistency results for optimal GQ design based on empirical distortion measures and stochastic optimization. These results statistically justify existing clustering algorithms in the domain of graphs. The proposed approach provides a template of how to link structural pattern recognition methods other than GQ to statistical pattern recognition.
---
PDF链接：
https://arxiv.org/pdf/1001.0921