Open AccessDimensional Reduction Techniques for Huge Volume of Data
Author(s) -
Soudagar Londhe,
Manasi Patil
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40572
Subject(s) - dimensionality reduction , computer science , volume (thermodynamics) , reduction (mathematics) , feature selection , data mining , data reduction , selection (genetic algorithm) , curse of dimensionality , artificial intelligence , data science , machine learning , mathematics , physics , geometry , quantum mechanics
Huge volume of data and information is needed with the expanding advancement in the current collection of tools, cloud storage, strategic techniques and increasing development of science technology. With the appearance of complete genome successions, the biomedical area has encountered an exceptional progression. This genomics has prompted the advancement of new high-produced strategies techniques that are huge amounts in measures of information and data, which inferred the exponential development of numerous organic and biological data sets. This paper represents different linear and non-linear dimensionality reduction techniques and their validity for different kinds of data information datasets and application regions. Keywords: High dimensional data, Dimensionality reduction, Linear techniques, Non-linear techniques, feature extraction, feature selection, Machine Learning