Dimensionality reduction techniques for high dimensional data: State of the art

Abstract

In the past decades, tons of data have been generated every single day as people increasingly rely on electronic products and networks in their lives. The developments in techniques and storage capabilities provide a fundamental condition for analyzing those huge-volumed data. Each captured feature represents a dimension of the data. So far, high dimensional data analysis has become a challenging task in various study fields. Redundant and irrelevant features can be removed by using different dimensionality reduction techniques. Effective information extraction can be achieved by using the proper method. This paper reviews the most widely used dimensionality reduction techniques and their application fields. It can be found that though DRTs have been successfully applied to many areas (i.e., image, audio/video data, biomedical), DRTs still need to be improved and developed to achieve better classification and prediction accuracy. Inter-method combinations will remain the focus of research in the future. Computation time and cost may not be a limitation anymore as the computation power of the computer is still developing. So, the development of the algorithm is becoming particularly important. This study provides a brief introduction to widely used DRTs and their variants, it will be helpful for understanding HDD analysis, and more DRTs will be researched in future work.