A Review of Dimensionality Reduction Techniques for Efficient Computation

Abstract

Dimensionality Reduction (DR) is the pre-processing step to remove redundant features, noisy and irrelevant data, in order to improve learning feature accuracy and reduce the training time. Dimensionality reductions techniques have been proposed and implemented by using feature selection and extraction method. Principal Component Analysis (PCA) one of the Dimensions reduction techniques which give reduced computation time for the learning process. In this paper presents most widely used feature extraction techniques such as EMD, PCA, and feature selection techniques such as correlation, LDA, forward selection have been analyzed based on high performance and accuracy. These techniques are highly applied in Deep Neural Network for medical image diagnosis and used to improve the classification accuracy. Further, we discussed how dimension reduction is made in deep learning.

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