Soft-competitive-growing classifier with unsupervised fine-tuning

In this paper a method for growing a Gaussian-mixture-based network is developed. The constructive technique is based on an EM algorithm to estimate the parameters and the number of nodes is iteratively increased by means of discriminant placement. The growth control is imposed by an information theoretic criterion that prevents the network from becoming extremely complex and losing generalization capabilities. After the growing phase is finished, another EM algorithm is used with labeled and unlabeled data in order to fine-tune network parameters. This solution improves the test-performance for the applications where labeled data is insufficient and the classes are not highly overlapped. We report results on some artificially generated examples and on terrain classification over a Landsat-TM image