Effects of Dimensionality Reduction on Classifier Training Time and Quality

Two important aspects when training a classifier are being investigated, training time and classifier quality. Two well-known dimensionality reduction techniques are performed on three groups of data to create smaller data sets for classifier training. The first two techniques are the principal component analysis (PCA) and its modification called adaptive PCA
$(\text{aPCA})$
. The third one is the linear discriminant analysis (LDA). Three groups of data sets are trained to create classifiers with and without being pre-processed with these feature reduction techniques. Training time and accuracy scores obtained from each classifier are compared, and the experiment results show that sample size and number of features are two important factors to be considered before selecting dimensionality reduction techniques. If the compatible data reduction technique and data set is chosen, the training time can be reduced without compromising the quality of the classifier.