| Title | Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Uzhga-Rebrov, O., Kuleshova, G. |
| Conference Name | 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) |
| Keywords | compositionality, cyber physical systems, Data analysis, decomposition, Eigenvalues and eigenfunctions, initial data set, left eigenvectors, Matrix converters, Matrix decomposition, matrix rank, Metrics, principal component analysis, pubcrawl, reduce dimensionality, right eigenvectors, singular value decomposition, singular value matrix, SVD, Symmetric matrices |
| Abstract | The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD). |
| DOI | 10.1109/ITMS51158.2020.9259304 |
| Citation Key | uzhga-rebrov_using_2020 |
Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set