CSIRO ICT Centre >> HAIL Seminar
Self-Organizing Map for Visual Analytics
Dr Masahiro Takatsuka
Tuesday 13 March 2007 at 11am
A Self-Organizing Map is a type of Artificial Neural Networks and has been primarily used for 1) Associative Memory and 2) Data Visualization. In either cases, the main function of the SOM is to model multivariate data. This modeling corresponds to learning the statistical structure of the multivariate data in a much simpler form (typically in a 2D space). During this process, the SOM will try to keep the topological structure of the original data as much as possible. Hence, it achieves its non-linear topological mapping from the high-dimensional space to the much lower (typically 2D) space. With this topological mapping capability, it allows us to visualize complex high-dimensional data structures.
Although the original form of the SOM does provide a useful visualization mechanism to a certain extent, it falls short of requirements in Visual Analytics. Visual Analytics is a emerging discipline, and it aims to utilize Visualization and User Interfaces in order to improve an Analytical Reasoning process. In this talk, SOM's shortcomings will be explained and some possible solutions and future challenges will be presented.
Masahiro Takatsuka is Senior Lecturer at the School of Information Technologies, the University of Sydney, where he heads the ViSLAB (Visualization Research Group). His current research interests include the use of manifold surfaces to multidimensional scaling and Information Visualization, Advanced Collaboration Technologies, in particular, the use of Service Oriented Remote Collaboration, and Network Centric Computer Graphics.
Takatsuka obtained his Ph.D. in Electrical and Computer Engineering from Monash University. He is a Member of the IEEE and ACM.