1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction
Tu T. Do, Mai Anh Vu, Tuan L. Vo +6
Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increas…
cs.LG2022
Principal Component Analysis based frameworks for efficient missing data imputation algorithms
Thu Nguyen, Hoang Thien Ly, Michael Alexander Riegler +2
Missing data is a commonly occurring problem in practice. Many imputation methods have been developed to fill in the missing entries. However, not all of them can scale to high-dim…