Various dimension reduction techniques for high dimensional data analysis: a review

In the era of healthcare, and its related research fields, the dimensionality problem of high dimensional data is a massive challenge as it contains a huge number of variables forming complex data matrices. The demand for dimension reduction of complex data is growing immensely to improvise data prediction, analysis and visualization. In general, dimension reduction techniques are defined as a compression of dataset from higher dimensional matrix to lower dimensional matrix. Several computational techniques have been implemented for data dimension reduction, which is further segregated into two categories such as feature extraction and feature selection. In this review, a detailed investigation of various feature extraction and feature selection methods has been carried out with a systematic comparison of several dimension reduction techniques for the analysis of high dimensional data and to overcome the problem of data loss. Then, some case studies are also cited to verify the better approach for data dimension reduction by considering few advances described in the technical literature. This review paper may guide researchers to choose the most effective method for satisfactory analysis of high dimensional data.

Automated summary

This review paper analyzes the challenges, demand, and technical methods for dimension reduction of high dimensional data, particularly focusing on feature extraction and feature selection techniques. Through systematic comparison and case studies, it suggests the best approach to address the issues in analyzing high dimensional data.