Data Dimension Reduction makes ML Algorithms efficient. (arXiv:2211.09392v1 [cs.CV])

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 …

algorithms arxiv data ml algorithms