Linear Discriminant Text Classification in High Dimension

Linear Discriminant (LD) techniques are typically used in pattern recognition tasks when there are many (n >> 10) datapoints in low-dimensional (d < 10) space. In this paper we argue on theoretical grounds that LD is in fact more appropriate when training data is sparse, and the dimension of the space is extremely high. To support this conclusion we present… CONTINUE READING