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Evaluation of Dimensionality Reduction Techniques for Big data

Publication Type : Conference Proceedings

Publisher : IEEE

Source : 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)

Url : https://ieeexplore.ieee.org/abstract/document/9076513

Campus : Amritapuri

School : School of Computing

Center : AI (Artificial Intelligence) and Distributed Systems

Year : 2020

Abstract : In this digital era, big data has very high dimension and requires large amount of space for its data storage. Hence a lossless data interpretation will be difficult when big data contains large dimension. But, all these dimensions in big data may not be relevant or they may be interrelated and hence redundancy may exist in attribute set. Dimensionality reduction is a technique which focusses on downsizing the attributes and complication of a high dimensional data. In this paper, a detailed study of different dimensionality reduction techniques namely principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), singular value decomposition (SVD), independent component analysis (ICA) has been proposed. Furthermore, it also provides comparative analysis based on various parameters.

Cite this Research Publication : R. Ramachandran, G. Ravichandran and A. Raveendran, "Evaluation of Dimensionality Reduction Techniques for Big data," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020.

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