Interpolative Decomposition

Interpolative decomposition (ID) is a matrix factorization technique used for low-rank approximation, feature selection, and pattern discovery in data. Current research focuses on Bayesian approaches to ID, employing Gibbs sampling for inference and incorporating techniques like automatic relevance determination (ARD) to improve model performance and feature selection capabilities. These Bayesian ID models are evaluated on diverse datasets, demonstrating improved accuracy in reconstructing data compared to non-Bayesian methods. The resulting advancements in ID have significant implications for various fields requiring efficient data dimensionality reduction and feature extraction.