The high-dimension, low-sample-size geometric representation holds under mild conditions

Biometrika

Volumn 94, Issue 3, 2007, Pages 760-766

  Ahn, Jeongyoun ^a   Marron, J S ^b   Muller, Keith M ^c   Chi, Yueh Yun ^d  

^a UNIVERSITY OF GEORGIA   (United States)

^b University of North Carolina at Chapel Hill   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d UNIVERSITY OF WASHINGTON   (United States)

Author keywords

High dimension; Large p small n; Linear discrimination; Low sample size; Sample covariance matrix

Indexed keywords

EID: 34548536094     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asm050     Document Type: Article

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