Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81714
Title: The Emerging "Big Dimensionality"
Authors: Zhai, Yiteng
Ong, Yew-Soon
Tsang, Ivor W.
Issue Date: 2014
Source: Zhai, Y., Ong, Y.-S., & Tsang, I. W. (2014). The Emerging "Big Dimensionality". IEEE Computational Intelligence Magazine, 9(3), 14-26.
Series/Report no.: IEEE Computational Intelligence Magazine
Abstract: The world continues to generate quintillion bytes of data daily, leading to the pressing needs for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a growing consensus among the computational intelligence communities that data volume presents an immediate challenge pertaining to the scalability issue. However, when addressing volume in Big Data analytics, researchers in the data analytics community have largely taken a one-sided study of volume, which is the "Big Instance Size" factor of the data. The flip side of volume which is the dimensionality factor of Big Data, on the other hand, has received much lesser attention. This article thus represents an attempt to fill in this gap and places special focus on this relatively under-explored topic of "Big Dimensionality", wherein the explosion of features (variables) brings about new challenges to computational intelligence. We begin with an analysis on the origins of Big Dimensionality. The evolution of feature dimensionality in the last two decades is then studied using popular data repositories considered in the data analytics and computational intelligence research communities. Subsequently, the state-of-the-art feature selection schemes reported in the field of computational intelligence are reviewed to reveal the inadequacies of existing approaches in keeping pace with the emerging phenomenon of Big Dimensionality. Last but not least, the "curse and blessing of Big Dimensionality" are delineated and deliberated.
URI: https://hdl.handle.net/10356/81714
http://hdl.handle.net/10220/39675
ISSN: 1556-603X
DOI: 10.1109/MCI.2014.2326099
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/MCI.2014.2326099].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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