Literature DB >> 16787738

Spherical self-organizing map using efficient indexed geodesic data structure.

Yingxin Wu1, Masahiro Takatsuka.   

Abstract

The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D rectangular grid data structure to store the icosahedron-based geodesic dome. Vertices relationships are maintained by their positions in the data structure rather than by immediate neighbor pointers or an adjacency list. Increasing the number of neurons can be done efficiently because the overhead caused by pointer updates is reduced. Experiments show that the spherical SOM using our data structure, called a GeoSOM, runs with comparable speed to the conventional 2D SOM. The GeoSOM also reduces data distortion due to removal of the boundaries. Furthermore, we developed an interface to project the GeoSOM onto the 2D plane using a cartographic approach, which gives users a global view of the spherical data map. Users can change the center of the 2D data map interactively. In the end, we compare the GeoSOM to the other spherical SOMs by space complexity and time complexity.

Entities:  

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Year:  2006        PMID: 16787738     DOI: 10.1016/j.neunet.2006.05.021

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  5 in total

1.  Multistrategy self-organizing map learning for classification problems.

Authors:  S Hasan; S M Shamsuddin
Journal:  Comput Intell Neurosci       Date:  2011-08-16

2.  The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

Authors:  Tonny J Oyana; Luke E K Achenie; Joon Heo
Journal:  Comput Math Methods Med       Date:  2012-03-19       Impact factor: 2.238

3.  Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map.

Authors:  Akimasa Seno; Tomonari Kasai; Masashi Ikeda; Arun Vaidyanath; Junko Masuda; Akifumi Mizutani; Hiroshi Murakami; Tetsuya Ishikawa; Masaharu Seno
Journal:  Cancer Inform       Date:  2016-08-16

4.  Modeling multisensory enhancement with self-organizing maps.

Authors:  Jacob G Martin; M Alex Meredith; Khurshid Ahmad
Journal:  Front Comput Neurosci       Date:  2009-06-24       Impact factor: 2.380

5.  Analysis of liver damage from radon, X-ray, or alcohol treatments in mice using a self-organizing map.

Authors:  Norie Kanzaki; Takahiro Kataoka; Reo Etani; Kaori Sasaoka; Akihiro Kanagawa; Kiyonori Yamaoka
Journal:  J Radiat Res       Date:  2016-09-10       Impact factor: 2.724

  5 in total

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