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Data Dimension Reduction makes ML Algorithms efficient. (arXiv:2211.09392v1 [cs.CV])
Nov. 18, 2022, 2:14 a.m. | Wisal Khan, Muhammad Turab, Waqas Ahmad, Syed Hasnat Ahmad, Kelash Kumar, Bin Luo
cs.CV updates on arXiv.org arxiv.org
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 …
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