Abstract
The self-organizing feature map (SOFM) is primarily used to map high-dimensional data into low-dimensional spaces for pattern classification applications. The pre-defined connections in the SOFM lattice and the weight adaptation algorithm enable topological associations to emerge within arbitrary numeric data. The degree of association or similarity between neighboring nodes on the lattice is largely influenced by mathematical and statistical measures between the data vectors assigned to the nodes. The relationship between neighboring nodes, or cluster units, can be visually interpreted by an observer if this information is displayed as colors and/or distortions on the SOFM lattice. This paper describes how a SOFM that starts as a tessellated unit sphere can develop a closed surface topology of arbitrary N -dimensional data vectors that reflects information content as defined by the mathematical or statistical measure. Transforming the numeric data into a closed geometric form enables the information embe...
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Smart Engineering System Design
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.