Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds

Ujjal Kumar Mukherjee, Snigdhansu Chatterjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper we present a new algorithm based on a weighted projection quantiles for fast and frugal real time quantile estimation of large sized high dimensional data clouds. We present a projection quantile regression algorithm for high dimensional data. Second, we present a fast algorithm for computing the depth of a point or a new observation in relation to any high-dimensional data cloud, and propose a ranking system for multivariate data. Third, we briefly describe a real time rapid monitoring scheme similar to statistical process monitoring, for actionable analytics with big data. We believe these algorithms would be very useful for real time analysis of high dimensional 'big data' sets including streaming data sets. The proposed algorithms would be of immense use in several application areas such as real time financial market analysis, real time remote health monitoring of patients using body area networked devices and real time pricing and inventory decisions in retail and manufacturing sector.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-71
Number of pages8
ISBN (Electronic)9781479956654
DOIs
StatePublished - Jan 1 2014
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period10/27/1410/30/14

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Keywords

  • 'Big Data'
  • body area network
  • data depth estimation
  • quantile regression
  • real time analysis
  • real time health monitoring
  • weighted projection quantiles

Cite this

Mukherjee, U. K., & Chatterjee, S. (2014). Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 64-71). [7004358] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004358