Pruning Rule for kMER-Based Acquisition of the Global Topographic Feature Map

Eiji UCHINO  Noriaki SUETAKE  Chuhei ISHIGAKI 

Publication
IEICE TRANSACTIONS on Information and Systems  Vol.E88-D  No.3  pp.675-678
Publication Date: 2005/03/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
Keyword: 
self-organizing map,  kernel-based topographic map,  kMER,  pruning rule, 

Full Text: PDF(703.5KB)

Summary: 
For a kernel-based topographic map formation, kMER (kernel-based maximum entropy learning rule) was proposed by Van Hulle, and some effective learning rules related to kMER have been proposed so far with many applications. However, no discusions have been made concerning the determination of the number of units in kMER. This letter describes a unit-pruning rule, which permits automatic contruction of an appropriate-sized map to acquire the global topographic features underlying the input data. The effectiveness and the validity of the present rule have been confirmed by some preliminary computer simulations.