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

Ujjal Kumar Mukherjee; Snigdhansu Chatterjee

doi:10.1109/BigData.2014.7004358

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
Editors	Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	64-71
Number of pages	8
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004358
State	Published - 2014
Externally published	Yes
Event	2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other	2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/Territory	United States
City	Washington
Period	10/27/14 → 10/30/14

Keywords

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

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Online availability

10.1109/BigData.2014.7004358

Library availability

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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). Article 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

Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds. / Mukherjee, Ujjal Kumar; Chatterjee, Snigdhansu.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Wo Chang; Jun Huan; Nick Cercone; Saumyadipta Pyne; Vasant Honavar; Jimmy Lin; Xiaohua Tony Hu; Charu Aggarwal; Bamshad Mobasher; Jian Pei; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2014. p. 64-71 7004358 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mukherjee, UK & 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, XT Hu, C Aggarwal, B Mobasher, J Pei & R Nambiar (eds), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004358, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 64-71, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004358

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

Mukherjee, Ujjal Kumar ; Chatterjee, Snigdhansu. / Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. editor / Wo Chang ; Jun Huan ; Nick Cercone ; Saumyadipta Pyne ; Vasant Honavar ; Jimmy Lin ; Xiaohua Tony Hu ; Charu Aggarwal ; Bamshad Mobasher ; Jian Pei ; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 64-71 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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

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Keywords

ASJC Scopus subject areas

Online availability

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