Intelligent Database Systems Lab N.Y.U.S.T. I. M. TurSOM: A Turing Inspired Self-organizing Map Presenter: Tsai Tzung Ruei Authors: Derek Beaton, Iren Valova, Dan MacLean IJCNN 2009 國立雲林科技大學 National Yunlin University of Science and Technology

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments Reference Data 2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation The traditional SOM is slower than TurSOM and need for post-processing methods for cluster identification. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To present a new variant of the SOM algorithm that utilizes two forms of selforganization:1) neurons, as in the classical Kohonen algorithm and 2) connections, as presented in Turing's model of Unorganized Machines. 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology TurSOM 5 Neuron Connection Turing Unorganized Machines Competitive Learning Techniques SOM algorithms

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Neuron responsibility Connection responsibility  The gap junction (GJ) mechanism 6 NeuronA r NeuronB Relative bigness NeuronC

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 7 Algorithmic Explanation B 100 C5C5 A 80

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Early TurSOM and double spiral problem 8 Purpose To test the hypothesis of connection reorganization being beneficial.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Full-featured TurSOM in handwriting experiment  TurSOM 9 Purpose To test the full-featured TurSOM on a sample from a handwriting dataset

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Full-featured TurSOM in handwriting experiment  typical one-dimensional SOM network 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments TurSOM 1D standardSOM 11 random the Peano-Iike convergence featuring single chain

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion MAJOR CINTRIBUTION  TurSOM displays behavior of a highly efficient SOM, in terms of both time and computational expense.  The TurSOM algorithm is applicable in a varying number of fields, just like the traditional SOM, but TurSOM lends itself more so to image processing and segmentation.  No post-processing methods are required in addition to TurSOM to detect distinct patterns, unlike other SOM algorithms, due to TurSOM‘s connection reorganization methods. FUTURE WORK  To take connection reorganization to scale (n-dimensional SOM networks). 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comment Advantage  Created a more efficient method Drawback  …… Application  SOM 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Reference Data   tw/%E5%9B%BE%E7%81%B5%E6%9C%BA 14