
For FullText PDF, please login, if you are a member of IEICE,
or go to Pay Per View on menu list, if you are a nonmember of IEICE.

On Evaluation of Reference Vector Density for SelfOrganizing Feature Map
Toshiyuki TANAKA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E77D
No.4
pp.402408 Publication Date: 1994/04/25 Online ISSN:
DOI: Print ISSN: 09168532 Type of Manuscript: Special Section PAPER (Special Issue on Neurocomputing) Category: Mapping Keyword: selforganization, feature map, asymptotic property, stability,
Full Text: PDF>>
Summary:
In this paper, I investigate a property of selforganizing feature map (SOFM) in terms of reference vector density q(x) when probability density function of input signal fed into SOFM is p(x). Difficulty of general analysis on this property is briefly discussed. Then, I employ an assumption (conformal map assumption) to evaluate this property, and it is shown that for equilibrium state, q(x)p(x)^{s} holds. By giving Lyapunov functioin for time evolution of reference vector density q(x) in SOFM, the equilibrium state is proved to be stable in terms of distribution. Comparison of the result with one which is based on different assumption reveals that there is no unique result of a simple form, such as conjectured by Kohonen. However, as there are cases in which these assumptions hold, these results suggest that we can consider a range of the property of SOFM. On the basis of it, we make comparison on this property between SOFM and fundamental adaptive vector quantization algorithm, in terms of the exponent s of the relation q(x)p(x)^{s}. Difference on this property between SOFM and fundamental adaptive vector quantization algorithm, and propriety of mean squared quantization error for a performance measure of SOFM, are discussed.

