Overview and comparative study of dimensionality reduction techniques for high dimensional data

Abstract The recent developments in the modern data collection tools, techniques, and storage capabilities are leading towards huge volume of data. The dimensions of data indicate the number of features that have been measured for each observation. It has become a challenging task to analyze high dimensional data. Different dimensionality reduction techniques are available in literature to eliminate irrelevant and redundant features. Selection of an appropriate dimension reduction technique can help to enhance the processing speed and reduce the time and effort required to extract valuable information. This paper presents the state-of-the art dimensionality reduction techniques and their suitability for different types of data and application areas. Furthermore, the issues of dimensionality reduction techniques have been highlighted that can affect the accuracy and relevance of results.

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