Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Clustering Algorithms
Toward Bayes-Optimal Linear Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Robust Quadratic Discriminant Function
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Precise Estimation of High-Dimensional Distribution and Its Application to Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Variational Bayesian mixture model on a subspace of exponential family distributions
IEEE Transactions on Neural Networks
Pattern recognition using boundary data of component distributions
Computers and Industrial Engineering
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In statistical pattern recognition, it is important to estimate the distribution of patterns precisely to achieve high recognition accuracy. In general, precise estimation of the parameters of the distribution requires a great number of sample patterns, especially when the feature vector obtained from the pattern is high-dimensional. For some pattern recognition problems, such as face recognition or character recognition, very high-dimensional feature vectors are necessary and there are always not enough sample patterns for estimating the parameters. In this paper, we focus on estimating the distribution of high-dimensional feature vectors with small number of sample patterns. First, we define a function, called simplified quadratic discriminant function (SQDF). SQDF can be estimated with small number of sample patterns and approximates the quadratic discriminant function (QDF). SQDF has fewer parameters and requires less computational time than QDF. The effectiveness of SQDF is confirmed by three types of experiments. Next, as an application of SQDF, we propose an algorithm for estimating the parameters of the normal mixture. The proposed algorithm is applied to face recognition and character recognition problems which require high-dimensional feature vectors.