High dimensional big data and pattern analysis: A tutorial

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Sensors and actuators embedded in physical objects being linked through wired/wireless networks known as "internet of things" are churning out huge volumes of data (McKinsey Quarterly report, 2010). This phenomenon has led to the archiving of mammoth amounts of data from scientific simulations in the physical sciences and bioinformatics, to social media and a plethora of other areas. It is predicted that over 30 billion devices with 200 billion intermittent connections will be connected by 2020. The creation and archival of the massive amounts of data spawned a multitude of industries. Data management and up-stream analytics is aided by data compression and dimensionality reduction. This review paper will focus on some foundational methods of dimensionality reduction by examining in extensive detail some of the main algorithms, and points the reader to emerging next generation methods that seek to identify structure within high dimensional data not captured by 2 nd order statistics.

Original languageEnglish
Title of host publicationBig Data Analytics - Second International Conference, BDA 2013, Proceedings
Pages68-85
Number of pages18
DOIs
StatePublished - 2013
Event2nd International Conference on Big Data Analytics, BDA 2013 - Mysore, India
Duration: 16 Dec 201318 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Big Data Analytics, BDA 2013
Country/TerritoryIndia
CityMysore
Period16/12/1318/12/13

Keywords

  • Canonical correlation Analysis
  • Dimensionality Reduction
  • Exploratory Projection Pursuit
  • Factor Analysis
  • Independent Component Analysis
  • Multivariate Analysis
  • Principal Component Analysis
  • Projections

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