Quantifying the neighborhood preservation of self-organizing feature maps.

Autor: Bauer HU; Inst. fur Theor. Phys., Frankfurt Univ., Pawelzik KR
Jazyk: angličtina
Zdroj: IEEE transactions on neural networks [IEEE Trans Neural Netw] 1992; Vol. 3 (4), pp. 570-9.
DOI: 10.1109/72.143371
Abstrakt: It is shown that a topographic product P, first introduced in nonlinear dynamics, is an appropriate measure of the preservation or violation of neighborhood relations. It is sensitive to large-scale violations of the neighborhood ordering, but does not account for neighborhood ordering distortions caused by varying areal magnification factors. A vanishing value of the topographic product indicates a perfect neighborhood preservation; negative (positive) values indicate a too small (too large) output space dimensionality. In a simple example of maps from a 2D input space onto 1D, 2D, and 3D output spaces, it is demonstrated how the topographic product picks the correct output space dimensionality. In a second example, 19D speech data are mapped onto various output spaces and it is found that a 3D output space (instead of 2D) seems to be optimally suited to the data. This is an agreement with a recent speech recognition experiment on the same data set.
Databáze: MEDLINE