Linear Subspace Learning-Based Dimensionality Reduction

Xudong Jiang

Abstract: The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate. An observed object is often represented by a high dimensional real-valued vector after some preprocessing while its class membership can be represented by a much lower dimensional binary vector. Thus, in the discriminating process, a pattern recognition system intrinsically reduces the dimensionality of the input data into the number of classes.