Probabilistic neural network with homogeneity testing in recognition of discrete patterns set

• Published in: Neural networks 2013-10, Vol.46, p.227-241
• Main Author: Savchenko, A.V.
• Format: Article
• Language: English
• Subjects: Algorithms; Authorship attribution; Computer Simulation; Discrete patterns set; Face recognition; Homogeneity testing; Models, Biological; Neural Networks (Computer); Pattern Recognition, Automated - methods; Probabilistic neural network; Probability; Recognition (Psychology) - physiology; Statistical pattern recognition
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2013.06.003

The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN.