Emergence in Self Organizing Feature Maps
Emergence in Self Organizing Feature Maps
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发表时间：
2007
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通讯作者：
A. Ultsch
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作者：
A. Ultsch
本文对新兴SOM(ESOM)不同于其他SOM的说法进行了一些解释。哲学和认识论中关于涌现的讨论是以公设的形式总结出来的。将SOM的性质与这些公设进行了比较。SOM满足大多数假设条件。最具批判性的假设是那些与“整体大于部分之和”有关的假设。关于这个问题的认识论假设即使不是不可能，也很难证明。本文提出了一种基于符号学概念的另一种公设，称为“符号学不可约性”。这一概念被应用于多神经元SOM上的U-矩阵。这导致了ESOM的定义为SOM产生一个非平凡的U-矩阵，在该U-矩阵上术语“分水岭”和“集水区”是有意义的，并且是集群一致的。证明了一种利用ESOM的涌现特性的聚类算法(U*C)优于其他流行的聚类算法。在盲目研究和现实世界应用中对合成数据的结果是令人信服的。
This paper sheds some light on the claim that Emergent SOM (ESOM) are different from other SOM. The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The most critical of the postulates are those concerned with "the whole is more than the sum of its parts". The epistemological postulates regarding this issue are hard, if not impossible, to prove. An alternative postulate relying on semiotic concepts, called "semiotic irreducibility" is proposed here. This concept is applied to U-Matrix on SOM with many neurons. This leads to the definition of ESOM as SOM producing a nontrivial U-Matrix on which the terms "watershed" and "catchment basin" are meaningful and which are cluster conform. It is demonstrated that a clustering algorithm (U*C) which exploits the emergent properties of such ESOM is superior to other popular clustering algorithms. Results on synthetic data in blind studies and a real world applications are convincing.