Abstract: The existing feature dimension reduction methods can roughly be categorized into two classes:feature extraction and feature selection.In feature extraction problems,the original features in the measurement space are initially transformed into a new dimension-reduced space via some specified transformation.Although the significant variables determined in the new space are related to the original variables,the physical interpretation in terms of the original variables may be lost.So,feature extraction will change the description of the original data.Unlike feature extraction,feature selection aims to seek optimal or suboptimal subsets of the original features by preserving the main information carried by the complete data to facilitate future analysis for high dimensional problems.Often,the selected features are a subset of the original features,and those insignificant and redundant features may be discarded.It is worth mentioning that almost all of the existing dimensionality reduction methods are not high fidelity methods.The result of these me-thods is only suitable for specific subsequent data analysis tasks,which is only a particular task under the preprocess.In this paper,with the technique of multiple hypothesis testing,we studied the dimensionality high fidelity reduction problem.The processing results can save all the useful information and eliminate the irrelevant features from the original data.

Key words: Feature selection,Dimension reduction,Multiple hypothesis testing