Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.

ABSTRACT: Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performan

SUBMITTER: Hung EC 

PROVIDER: S-EPMC11257416 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature