Title: Dimensionality reduction with missing values imputation,
Abstract: In this study, we propose a new statical approach for high-dimensionality
reduction of heterogenous data that limits the curse of dimensionality and
deals with missing values. To handle these latter, we propose to use the Random
Forest imputation's method. The main purpose here is to extract useful
information and so reducing the search space to facilitate the data exploration
process. Several illustrative numeric examples, using data coming from publicly
available machine learning repositories are also included. The experimental
component of the study shows the efficiency of the proposed analytical
approach.