.png){kind=icon}
返回
MDL principle for robust vector quantisation
.png){kind=icon}
PRE
AI
DOI：10.1007/s100440050015.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We address the problem of finding the optimal number of reference vectors for vector quantisation from the point of view of the Minimum Description Length (MDL) principle. We formulate vector quantisation in terms of the MDL principle, and then derive different instantiations of the algorithm, depending on the coding procedure. Moreover, we develop an efficient algorithm (similar to EM-type algorithms) for optimising the MDL criterion. In addition, we use the MDL principle to increase the robustness of the training algorithm, namely, the MDL principle provides a criterion to decide which data points are outliers. We illustrate our approach on 2D clustering problems (in order to visualise the behaviour of the algorithm), and present applications on image coding. Finally, we outline various ways to extend the algorithm.
Keyword:
clustering
colour-image segmentation
image coding
minimum description length
robustness
vector quantisation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
