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

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PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS (2014, background): “, Johnstone (2001) and Paul (2007)), and is extended to the HDLSS context by Ahn et al. (2007) and Jung and Marron (2009).”
An Overview on the Estimation of Large Covariance and Precision Matrices (2015, unknown): “Such an approach has drawn growing attentions in the recent literature on high-dimensional PCA (e.g., Jung and Marron (2009); Shen et al. (2013a,b); Ahn et al. (2007)).”
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data (2008, background): “For example, Ahn, Marron, Muller, and Chi (2007) showed that for D = diag (d α , 1 ,..., 1 ) with a ﬁxed n , the ﬁrst sample eigenvalue is approximately distributed as 1 n d α χ 2 n for large d .”
Two-Stage Procedures for High-Dimensional Data (2011, background): “(2005), Ahn et al. (2007), and Yata and Aoshima (2012).”
PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context (2009, background): “In recent years, substantial work has been done on the asymptotic behavior of eigenvalues of the sample covariance matrix in the limit as d → ∞, see Johnstone (2001), Baik et al.”
PCA CONSISTENCY IN HIGH DIMENSION, LOW SAMPLE SIZE CONTEXT (2009, unknown): “[1].”