Linear Subspace Learning-Based Dimensionality Reduction
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.