A Unified Approach to Feature Selection and Learning in Unsupervised Environments

Here the twin problems of feature selection and learning are tackled simultaneously to obtain a unified approach to the problem of pattern recognition in an unsupervised environment. This is achieved by combining a feature selection scheme based on the stochastic learning automata model with an unsupervised learning scheme such as learning with a probabilistic teacher. Test implementation of this scheme using the remotely sensed agricultural data of the Purdue laboratory for agricultural remote sensing (LARS) in a simulated unsupervised mode, has brought out the efficacy of this integrated system of feature selection and learning.