Data Preprocessing Questions
Data imputation using singular value decomposition (SVD) is a technique used in data preprocessing to fill in missing values in a dataset. SVD is a matrix factorization method that decomposes a matrix into three separate matrices: U, Σ, and V.
In the context of data imputation, SVD is applied to the dataset with missing values, and the missing values are estimated by reconstructing the matrix using the decomposed matrices. The reconstructed matrix provides estimates for the missing values based on the patterns and relationships present in the observed data.
By utilizing SVD, data imputation can be performed effectively even when there are missing values in multiple variables or across different dimensions of the dataset. This technique helps to minimize the impact of missing data on subsequent analysis or modeling tasks, ensuring a more complete and reliable dataset for further analysis.