Title: A novel high-dimensional data dimension reduction algorithm based on ameliorated supportive vector machine

Authors: Weiting Yue; Qiaolian Shen

Addresses: Information Engineering School, Hangzhou Dianzi University, Zhejiang, 310018, China ' Zhejiang Gas and Thermoelectricity Design Institute, Zhejiang, 310001, China

Abstract: Along with the rapid advancement of computer science and data engineering, the complexity and dimension of the data considered are bursting. With the increase of the data dimension, the performance of high dimensional index structure drops rapidly. And these high-dimensional data usually contains many redundant, its essence dimension often is much smaller than the original data, so the high-dimensional data processing question boils down to the related dimension reduction method to cut some less relevant data and reduce its dimension. This paper integrates the ameliorated supportive vector machine to propose the novel high-dimensional data dimension reduction algorithm. Our algorithm optimises the traditional methodologies from the three layers: 1) data cleaning and pre-processing steps are added to enhance the feasibility of the captured data; 2) SVM is modified to hold the feature of higher robustness; 3) application scenarios are built to test the performance. The experimental result proves that the proposed method outperforms compared with other state-of-theart algorithms theoretically and numerically.

Keywords: data dimension; supportive vector machine; optimisation; high-dimensional; application.

DOI: 10.1504/IJNVO.2017.085538

International Journal of Networking and Virtual Organisations, 2017 Vol.17 No.2/3, pp.252 - 267

Received: 25 Jul 2016
Accepted: 13 Sep 2016

Published online: 30 Jul 2017 *

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