.png){kind=icon}
返回
Self-organizing maps with information theoretic learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2013.12.059.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The self-organizing map (SUM) is one of the popular clustering and data visualization algorithms and has evolved as a useful tool in pattern recognition, data mining since it was first introduced by Kohonen. However, it is observed that the magnification factor for such mappings deviates from the informationtheoretically optimal value of 1 (for the SUM it is 2/3). This can be attributed to the Use of the mean square error to adapt the system, which distorts the mapping by oversampling the low probability regions. In this work, we first discuss the kernel SUM in terms of a similarity measure called correntropy induced metric (CIM) and empirically show that this can enhance the magnification of the mapping without much increase in the computational complexity of the algorithm. We also show that adapting the SUM in the CIM sense is equivalent to reducing the localized cross information potential, an informationtheoretic function that quantifies the similarity between two probability distributions. Using this property we propose a kernel bandwidth adaptation algorithm for Gaussian kernels, with both homoscedastic and heteroscedastic components. We show that the proposed model can achieve a mapping with optimal magnification and can automatically adapt the parameters of the kernel function. (C) 2014 Published by Elsevier B.V.
Keyword:
SOM
Kernel methods
Information theoretic learning
Magnification factor
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。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Avascular Necrosis With Complete Resorption of the Proximal Humerus Following Undisplaced Three-Part Fracture.png)
Orthopedics
OrthopedicsIF0
PREAI
分享收藏
分享收藏
查看更多内容