About CUR Matrix Decomposition

CUR Matrix Decomposition is a low-rank matrix decomposition algorithm that is explicitly expressed in a small number of actual columns and/or actual rows of data matrix.

CUR Matrix Decomposition was developed as an alternative to Singular Value Decomposition (SVD) and Principal Component Analysis (PCA). CUR Matrix Decomposition selects columns and rows that exhibit high statistical leverage or large influence from the data matrix. By implementing the CUR Matrix Decomposition algorithm, a small number of most important attributes and/or rows can be identified from the original data matrix. Therefore, CUR Matrix Decomposition is an important tool for exploratory data analysis. CUR Matrix Decomposition can be applied to a variety of areas and facilitates regression, classification, and clustering.