Classification using distance-based segmentation-application to the analysis of EEG signals
Pattern Recognition Letters
Unsupervised learning through symbolic clustering
Pattern Recognition Letters
Analysis and design of decision-directed learning schemes using stochastic approximation
Information Sciences: an International Journal
| Hi-index | 754.84 |
A Bayes approach to nonsupervised pattern recognition is given wheren l-dimensional vector samplesX_{1}, X_{2}, cdots , X_{n}are received unclassified, i.e., any one ofMpattern sourcesomega_{1}, omega_{2}, cdots, omega_{M}, with corresponding probabilities of occurrenceQ_{1_{o}}, Q_{2_{o}} , cdots , Q_{M_{o}}, caused each sampleX_{s}, s=1,2, cdots , n. The approach utilizes the fact that the cumulative distribution function (c.d.f.) ofX_{s}is a mixture c.d.f.,F(X_{s})= sum_{i=1}^{M} F(X_{s}|omega_{i}) Q_{i_{o}}. It is assumed that available a priori knowledge includes knowledge ofMand the family{F(X_{s}|omega_{i})}, whereF(X_{s}|omega_{i})is characterized by a vectorB_{i_{o}}. In general,B_{i_{o}}andQ_{i_{o}}, i = 1,2, cdots , Mare considered fixed but unknown, and conditional probability of error in deciding which source causedX_{n}is minimized. When the functional form ofF(X_{s}|omega_{i})in terms ofB_{i_{o}}is unknown, the family{F(X_{s}|omega_{i})}is taken to be the family of multinomial c.d.f.'s--an application of the histogram concept to the nonsupervisory problem. Additional nonparameteric a priori knowledge about the family--such asF(X_{s}|omega_{i})is symmetrical, and/orF(X_{s}|omega_{i})differs fromF(X_{s}|omega_{j})only by a translational vector--can be utilized in the Bayes solution.