High dimensional data mining and visualization via gaussianization

摘要

A method is provided for providing high dimensional data. The high dimensional data is linearly transformed into less dependent coordinates, by applying a linear transform of n rows by n columns to the high dimensional data. Each of the coordinates are marginally Gaussianized, the Gaussianization being characterized by univariate Gaussian means, priors, and variances. The transforming and Gaussianizing steps are iteratively repeated until the coordinates converge to a standard Gaussian distribution. The coordinates of all iterations are arranged hierarchically to facilitate data mining. The arranged coordinates are then mined. According to an embodiment of the invention, the transform step includes applying an iterative maximum likelihood expectation maximization (EM) method to the high dimensional data.