Geometric Representation of High Dimension, Low Sample Size Data 论文

2005Journal of the Royal Statistical Society Series B (Statistical Methodology)引用 501

Random Matrices and ApplicationsStochastic processes and statistical mechanicsBayesian Methods and Mixture Models

Bayesian Methods and Mixture Models Stochastic processes and statistical mechanics Random Matrices and Applications

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Summary High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This geometric representation is used to obtain several new statistical insights.

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Amnon Neeman

Peter A. Hall

Geometric Representation of High Dimension, Low Sample Size Data 论文

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