SDFS: A Standardization Technique for Nonparametric Analysis

Abstract

Due to availability of computational tools for data acquisition, it is very easy to collect many dimensions from an object. Nevertheless, data acquisition from an object in an experiment may have a low number of dimensions. The analysis of low dimensional data has break-through role. But raw and sparse nature of dataset imposes new challenges and requirements for data analysis due to their special and unique characteristics. In the process of overall characterization of low-dimensional data, the data pre-processing plays crucial role. One of the first processes is normalization and standardization process. Therefore, in this paper, I would like to propose novel standardization technique called SDFS (Standardization for Distribution Free Statistics) for nonparametric data analysis. This technique is robust for small sample size with missing values of data points, which commonly exist in real time experiments lead to sparse low-dimensional data.  The comprehensive experimental evaluation shows that SDFS standardization is significantly outperforms on existing standardization methods.