A Gaussian Mixture Model Classifier Using Supervised And Unsupervised Learning
G.L. Goodman, Daniel W. McMichael · 1996
Topic category: Image and Multi-dimensional Signal Processing Statistical Signal & Array Processing This paper presents an algorithm for a maximum likelihood estimation (MLE) classifier, using Gaussian mixture models (GMMs), incorporating a combination of supervised and unsupervised training. This will enable the use of data for which no ground truth class labels are available, to improve classifier performance. The applications motivation for this work is to improve the performance of military unattended ground sensors (UGS), used for the detection of' hostile intruders in remote regions. There is considerable scope for the application of multi-sensor data fusion techniques to UGS systems. The algorithm presented in this paper uses GMMs to represent classes of targets in an UGS system. See [l], [2], [3], [4], [5] for previous work relevant to this paper. Parameter values for the GMMs are obtained using MLE.