Abstract
With the increasing use of Machine day by day, data analysts’ job has increased drastically. With the data gathered from millions of machines and sensors, modern day datasets becomes wealthier in information. This makes the data to be high dimensional and it is quite common to see datasets with hundreds of features. One of the biggest problems that data analysts face is dealt with high dimensional data. Without a major loss of information, data can be effectively reduced to a much smaller number of variables. This method of reducing variables is known as Dimensionality Reduction. The objective of this paper is to review methods used for reducing Dimensionality.
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Chauhan, D., Mathews, R. (2020). Review on Dimensionality Reduction Techniques. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_41
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DOI: https://doi.org/10.1007/978-3-030-43192-1_41
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