Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns.  The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space.  A pattern recognition system typically comprises a sensor that gathers the observations to be classified or described, a feature extraction mechanism that computes numeric or symbolic information from the observations, and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.  Pattern recognition systems are commonly used in speech recognition, document classification, shape recognition, and handwriting recognition. 
The Modified Quadratic Discriminant Function (MQDF) is a statistical classification method that can be applied to the problem of pattern recognition, such as handwritten character recognition.  MQDF classifiers can scale well with a large number of output classes, which makes them well suited to East Asian character recognition because East Asian languages include tens of thousands characters.  An MQDF classifier stores a variable number of means, eigenvalues and eigenvectors that describe the characteristics of each class.  The eigenvectors are chosen using principal component analysis of covariance matrices generated per class, and so are often referred to as principal components. 
Commonly available implementations of MQDF classifiers store the same number of principal components for all output classes.  One significant drawback of the existing MQDF classifier implementations is a large memory footprint.  It is possible to reduce the memory footprint by reducing the number of principal components stored for each class, but reducing the number of principal components beyond a certain point may result in unacceptable classification accuracy degradation.