Beta Scale Invariant Map

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• 作者：Hé ; ctor Quintiá ; n ; ^{hector.quintian@usal.es} ; ; Emilio Corchado ^{escorchado@usal.es}
• 关键词：Topology preserving maps ; Quality measures ; SIM ; MLHL-SIM ; ViSOM ; GNG ; Beta distribution ; Imbalanced datasets
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：59
• 期：Complete
• 页码：218-235
• 全文大小：2396 K
• 卷排序：59

文摘

In this study we present a novel version of the Scale Invariant Map (SIM) called Beta-SIM, developed to facilitate the clustering and visualization of the internal structure of complex datasets effectively and efficiently. It is based on the application of a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution, when applied to the Scale Invariant Map. The Beta-SIM behavior is thoroughly analyzed and successfully demonstrated over 2 artificial and 16 real datasets, comparing its results, in terms of three performance quality measures with other well-known topology preserving models such as Self Organizing Maps (SOM), Scale Invariant Map (SIM), Maximum Likelihood Hebbian Learning-SIM (MLHL-SIM), Visualization Induced SOM (ViSOM), and Growing Neural Gas (GNG). Promising results were found for Beta-SIM, particularly when dealing with highly complex datasets.