| Title | A Novel Correlation-Based CUR Matrix Decomposition Method |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Hemmati, A., Nasiri, H., Haeri, M. A., Ebadzadeh, M. M. |
| Conference Name | 2020 6th International Conference on Web Research (ICWR) |
| Keywords | compositionality, correlation theory, CUR Matrix Decomposition, CUR matrix decomposition method, cyber physical systems, data matrix, decomposition, high-dimensional data, important approximation technique, Low-Rank Approximations, matrix approximation, Matrix decomposition, matrix decomposition techniques, Metrics, novel correlation-based CUR matrix decomposition method, pubcrawl, singular value decomposition, web data |
| Abstract | Web data such as documents, images, and videos are examples of large matrices. To deal with such matrices, one may use matrix decomposition techniques. As such, CUR matrix decomposition is an important approximation technique for high-dimensional data. It approximates a data matrix by selecting a few of its rows and columns. However, a problem faced by most CUR decomposition matrix methods is that they ignore the correlation among columns (rows), which gives them lesser chance to be selected; even though, they might be appropriate candidates for basis vectors. In this paper, a novel CUR matrix decomposition method is proposed, in which calculation of the correlation, boosts the chance of selecting such columns (rows). Experimental results indicate that in comparison with other methods, this one has had higher accuracy in matrix approximation. |
| DOI | 10.1109/ICWR49608.2020.9122286 |
| Citation Key | hemmati_novel_2020 |
A Novel Correlation-Based CUR Matrix Decomposition Method