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Various dimension reduction techniques for high dimensional data analysis: a review
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2021-01-08 , DOI: 10.1007/s10462-020-09928-0
Papia Ray , S. Surender Reddy , Tuhina Banerjee

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.

中文翻译:

用于高维数据分析的各种降维技术:综述

在医疗保健时代及其相关研究领域,高维数据的维数问题是一个巨大的挑战,因为它包含形成复杂数据矩阵的大量变量。为了即兴进行数据预测、分析和可视化,对复杂数据降维的需求正在急剧增长。通常,降维技术被定义为将数据集从高维矩阵压缩到低维矩阵。已经实现了几种用于数据降维的计算技术,将其进一步分为两类,例如特征提取和特征选择。在这次审查中,对各种特征提取和特征选择方法进行了详细研究,并对用于分析高维数据和克服数据丢失问题的几种降维技术进行了系统比较。然后,还引用了一些案例研究,通过考虑技术文献中描述的一些进步来验证更好的数据降维方法。这篇综述论文可以指导研究人员选择最有效的方法对高维数据进行满意的分析。
更新日期:2021-01-08
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