Reduction of processing time for optimal and quadratic discriminant analyses

摘要

A fast algorithm is presented for optimal discriminant analysis and quadratic discriminant analysis. In this algorithm, the discriminant function of an input feature vector for each category is calculated via a monotonically increasing sequence, and when the sequence value exceeds a certain value, then you can assert that the current category cannot be the classification result and omit the redundant calculation of the remaining terms for the category, thus making the calculation faster. Applying this algorithm to the recognition experiment on handwritten characters, we could reduce the processing time to 4% of the conventional simple method. Since both discriminant analyses assume the normal distribution of the features, disnormality contained in real-world data affects the accuracy of the two discriminant analyses. We also compared the accuracy performances of the two discriminant analyses using real-world data and artificial data.