On a general two-sample test with new critical value for multivariate feature selection in chest X-Ray image analysis problem

In this paper we propose a Two-Sample Test for the means of high dimensional data and a new method to calculate the critical value. The proposed test does not require any condition linking the data dimension and the sample size which makes it a good alternative to the Hotelling T² statistic when the data dimension is much larger than the sample size and/or the two sample covariance matrices are not equal. One of the most important application of the proposed test is multivariate feature selection in all fields specially where data dimension is high like image features, genes or finance data. It is also important to highlight the low computing time required by the proposed method to calculate the critical value.