Initial Dataset Dimension Reduction Using Principal Component Analysis

2020
Oļegs Užga-Rebrovs, Gaļina Kuļešova

Any data in an implicit form contain information of interest to the researcher. The purpose of data analysis is to extract this information. The original data may contain redundant elements and noise, distorting these data to one degree or another. Therefore, it seems necessary to subject the data to preliminary processing. Reducing the dimension of the initial data makes it possible to remove interfering factors and present the data in a form suitable for further analysis. The paper considers an approach to reducing the dimensionality of the original data based on principal component analysis.

Keywords
Data labels in the space of principal components, data recovery in a space of lower dimension, data transformation into a space of principal components, eigenvectors and eigenvalues of variance/covariance matrix, variance/covariance matrix of data.
DOI
10.7250/itms-2020-0006
Hyperlink
https://itms-journals.rtu.lv/article/view/itms-2020-0006

Užga-Rebrovs, O., Kuļešova, G. Initial Dataset Dimension Reduction Using Principal Component Analysis. Information Technology and Management Science, 2020, Vol. 23, pp. 41-44. e-ISSN 2255-9094. Available from: doi:10.7250/itms-2020-0006

Publication language
English (en)