A survey on the cures for the curse of dimensionality in big data

Abstract

Dimensionality reduction techniques are used to reduce the complexity for analysis of high-dimensional data sets. The raw input data set may have large dimensions, and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. Hence, using dimensionality reduction techniques, one can reduce the dimensions of input data toward accurate prediction with less cost. In this paper, the different machine learning approaches used for dimensionality reductions such as principal component analysis (PCA), singular value decomposition, linear discriminant analysis, Kernel PCA, and artificial neural network have been studied.