Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics

Jasmine Kah Phooi Seng; Kenneth Li-Minn Ang

doi:10.1109/ACCESS.2017.2726543

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Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics

Journal article

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Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics

Jasmine Kah Phooi Seng and Kenneth Li-Minn Ang

IEEE Access, Vol.5, pp.14056-14065

2017

DOI: https://doi.org/10.1109/ACCESS.2017.2726543

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Abstract

Decision making

Big data

classification

computational complexity

feature extraction

linear discriminant analysis

This paper introduces a novel big feature data analytics scheme for integration toward data analytics with decision making. In this scheme, a split and combine approach for a linear discriminant analysis (LDA) algorithm termed SC-LDA is proposed. The SC-LDA replaces the full eigenvector decomposition of LDA with much cheaper eigenvector decompositions on smaller sub-matrices, and then recombines the intermediate results to obtain the exact reconstruction as for the original algorithm. The splitting or decomposition can be further applied recursively to obtain a multi-stage SC-LDA algorithm. The smaller sub-matrices can then be computed in parallel to reduce the time complexity for big data applications. The approach is discussed for an LDA algorithm variation (LDA/QR), which is suitable for the analytics of Big Feature data sets. The projected data vectors into the LDA subspace can then be integrated toward the decision-making process involving classification. Experiments are conducted on real-world data sets to confirm that our approach allows the LDA problem to be divided into the size-reduced sub-problems and can be solved in parallel while giving an exact reconstruction as for the original LDA/QR.

Details

Title: Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics
Authors: Jasmine Kah Phooi Seng (Author) - 61USC_INST___CSU
Kenneth Li-Minn Ang (Corresponding Author) - 61USC_INST___CSU
Publication details: IEEE Access, Vol.5, pp.14056-14065
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2017
DOI: 10.1109/ACCESS.2017.2726543
ISSN: 2169-3536; 2169-3536
Organisation Unit: School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513893802621
Output Type: Journal article

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