ℓ1 Major Component Detection and Analysis (ℓ1 MCDA) in Three and Higher Dimensional Spaces

ℓ1 Major Component Detection and Analysis (ℓ1 MCDA) in Three and Higher Dimensional Spaces

Based on the recent development of two dimensional ℓ1 major component detection and analysis (ℓ1 MCDA), we develop a scalable ℓ1 MCDA in the n-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with irregularly positioned “spokes” and “clutters”....

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Bibliographic Details
Main Authors: Zhibin Deng, John E. Lavery, Shu-Cherng Fang, Jian Luo
Format: Article
Language:English
Published: MDPI AG 2014-08-01
Series:Algorithms
Subjects:
multidimensional heavy-tailed distribution
ℓ1-norm
major component
n-dimensional
outlier
pattern recognition
robust principal component analysis
Online Access:http://www.mdpi.com/1999-4893/7/3/429

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http://www.mdpi.com/1999-4893/7/3/429

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