Towards ultrahigh dimensional feature selection for big data

Towards ultrahigh dimensional feature selection for big data

In this paper, we present a new adaptive feature scaling scheme for ultrahigh-dimensional feature selection on Big Data, and then reformulate it as a convex semi-infinite programming (SIP) problem. To address the SIP, we propose an eficient feature generating paradigm. Different from traditional gra...

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Bibliographic Details
Main Authors: Tan, Mingkui, Tsang, Ivor W., Wang, Li
Other Authors: School of Computer Engineering
Format: Journal Article
Language:English
Published: 2014
Subjects:
DRNTU::Engineering::Computer science and engineering::Data
Online Access:https://hdl.handle.net/10356/105805
http://hdl.handle.net/10220/20902
http://www.jmlr.org/papers/v15/tan14a.html

Internet

https://hdl.handle.net/10356/105805
http://hdl.handle.net/10220/20902
http://www.jmlr.org/papers/v15/tan14a.html

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