An Experimental Study of Some Algorithms for Unsupervised Learning

Three well-known algorithms for unsupervised learning using a decision-directed approach are the random labeling of patterns according to the estimated a posteriori probabilities, the classification according to the estimated a posteriori probabilities, and the iterative solution of the maximum likelihood equations. The convergence properties of these algorithms are studied by using a sample of about 10 000 handwritten numerals. It turns out that the iterative solution of the maximum likelihood equations has the best properties among the three approaches. However, even this one fails to yield satisfactory results if the number of unknown parameters becomes large, as is usually the case in realistic problems of pattern recognition.