hSOM: Visualizing Self-Organizing Maps to Accomodate Categorical Data

被引：0

作者：

Kilgore, Phillip C. S. R. ^{[1
]}

Trutschl, Marjan ^{[1
]}

Cvek, Urska ^{[1
]}

Nam, Hyung W. ^{[2
]}

机构：

[1] Louisiana State Univ, Dept Comp Sci, Shreveport, LA 71105 USA

[2] Louisiana State Univ Hlth Shreveport, Dept Pharmacol Toxicolol & Neurosci, Shreveport, LA USA

来源：

2020 24TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV 2020) | 2020年

基金：

美国国家卫生研究院;

关键词：

self-organizing map; visualization; unsupervised learning; histogram; discrete; neural network; artificial intelligence;

D O I：

10.1109/IV51561.2020.00111

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Kohonen's self-organizing map is an unsupervised machine learning method designed to preserve the topology of its input space. Although this method has been used to efficiently summarize multidimensional data, the visualization of its constituent data has received less attention. We propose a method of addressing the visualization problem by augmenting a classical self-organizing map visualization to include an embedded histogram and evaluate its utility in depicting the self-organizing maps's constituents categorized by a discrete variable.

引用

页码：644 / 650

页数：7

共 15 条

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