  The central goal of this paper is to establish two commonly available
dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor
Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe
their application in several datasets. These DR techniques are applied to nine
different datasets namely CNAE9, Segmentation, Seeds, Pima Indians diabetes,
Parkinsons, Movement Libras, Mammographic Masses, Knowledge, and Ionosphere
acquired from UCI machine learning repository. By applying t-SNE and MDS
algorithms, each dataset is transformed to the half of its original dimension
by eliminating unnecessary features from the datasets. Subsequently, these
datasets with reduced dimensions are fed into three supervised classification
algorithms for classification. These classification algorithms are K Nearest
Neighbors (KNN), Extended Nearest Neighbors (ENN), and Support Vector Machine
(SVM). Again, all these algorithms are implemented in Matlab. The training and
test data ratios are maintained as ninety percent: ten percent for each
dataset. Upon accuracy observation, the efficiency for every dimensionality
technique with availed classification algorithms is analyzed and the
performance of each classifier is evaluated.
