Open AccessA SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA
Author(s) -
Reshma Remesh,
Pattabiraman
Publication year - 2017
Publication title -
asian journal of pharmaceutical and clinical research
Language(s) - English
Resource type - Journals
eISSN - 2455-3891
pISSN - 0974-2441
DOI - 10.22159/ajpcr.2017.v10s1.19755
Subject(s) - dimensionality reduction , principal component analysis , curse of dimensionality , artificial neural network , artificial intelligence , computer science , data set , raw data , machine learning , set (abstract data type) , singular value decomposition , pattern recognition (psychology) , data mining , programming language
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. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network have been studied.