A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata | Zendy

R. Krishnan | Zendy; V. A. Samaranayake | Zendy; S. Jagannathan | Zendy

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A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata

Author(s) -

R. Krishnan,

V. A. Samaranayake,

S. Jagannathan

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.10.507

Subject(s) - singular value decomposition , computer science , dimension (graph theory) , nonlinear system , reduction (mathematics) , big data , dimensionality reduction , rank (graph theory) , covariance , parametric statistics , singular value , process (computing) , algorithm , data mining , artificial intelligence , mathematics , statistics , eigenvalues and eigenvectors , physics , geometry , quantum mechanics , pure mathematics , combinatorics , operating system

In this paper, a multi-step dimension-reduction approach is proposed for addressing nonlinear relationships within attributes. In this work, the attributes in the data are first organized into groups. In each group, the dimensions are reduced via a parametric mapping that takes into account nonlinear relationships. Mapping parameters are estimated using a low rank singular value decomposition (SVD) of distance covariance. Subsequently, the attributes are reorganized into groups based on the magnitude of their respective singular values. The group-wise organization and the subsequent reduction process is performed for multiple steps until a singular value-based user-defined criterion is satisfied. Simulation analysis is utilized to investigate the performance with five big data-sets.

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