  The semantics are derived from textual data that provide representations for
Machine Learning algorithms. These representations are interpretable form of
high dimensional sparse matrix that are given as an input to the machine
learning algorithms. Since learning methods are broadly classified as
parametric and non-parametric learning methods, in this paper we provide the
effects of these type of algorithms on the high dimensional sparse matrix
representations. In order to derive the representations from the text data, we
have considered TF-IDF representation with valid reason in the paper. We have
formed representations of 50, 100, 500, 1000 and 5000 dimensions respectively
over which we have performed classification using Linear Discriminant Analysis
and Naive Bayes as parametric learning method, Decision Tree and Support Vector
Machines as non-parametric learning method. We have later provided the metrics
on every single dimension of the representation and effect of every single
algorithm detailed in this paper.
