SMLSOM: The shrinking maximum likelihood self-organizing map

Maximum Likelihood Optimization of Self-Organizing Map Parameters

Lifestyle patterns in the Iranian population: Self- organizing map application

Background: The present study evaluated the lifestyle behavior patterns and its associations with demographic factors in the Iranian population. Methods: A total of 8244 people aged 25-70 years who participated in a national survey in 2011 were included in the study. Factors related to lifestyle (such as diet, physical activity, and tobacco use) have been collected using a questionnaire. A sel...

The Time Adaptive Self Organizing Map for Distribution Estimation

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

The Self-organizing Map

information from multidimensional primary signals, and to represent it as a location, say, in a two-dimensional network. Although this i s already a step towards generalization and symbolism, it must be admitted that the extraction of features from geometrically or physically relatable data elements i s still a very concrete task, in principle at least. Theoperation of the brain at the higher l...

Maximum Likelihood Topographic Map Formation

We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.