  Data dimension reduction (DDR) is all about mapping data from high dimensions
to low dimensions, various techniques of DDR are being used for image dimension
reduction like Random Projections, Principal Component Analysis (PCA), the
Variance approach, LSA-Transform, the Combined and Direct approaches, and the
New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping.
In this paper, we demonstrate that pre-processing not only speeds up the
algorithms but also improves accuracy in both supervised and unsupervised
learning. In pre-processing of DDR, first PCA based DDR is used for supervised
learning, then we explore AE based DDR for unsupervised learning. In PCA based
DDR, we first compare supervised learning algorithms accuracy and time before
and after applying PCA. Similarly, in AE based DDR, we compare unsupervised
learning algorithm accuracy and time before and after AE representation
learning. Supervised learning algorithms including support-vector machines
(SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic
Gradient Descent classifier (SGDC) and unsupervised learning algorithm
including K-means clustering, are used for classification purpose. We used two
datasets MNIST and FashionMNIST Our experiment shows that there is massive
improvement in accuracy and time reduction after pre-processing in both
supervised and unsupervised learning.
