Latent Factor Analysis

Latent factor analysis (LFA) aims to uncover hidden factors underlying observed data by decomposing it into lower-dimensional representations. Current research emphasizes improving LFA's capabilities, particularly for high-dimensional and incomplete data, through advancements in model architectures like non-negative LFA (NLFA) and its symmetric variants, often incorporating techniques such as dynamic bias incorporation, multi-constraints, and adaptive divergence measures to enhance accuracy and efficiency. These improvements are driven by the need for robust and interpretable methods for analyzing complex datasets in diverse fields, including network analysis, bioinformatics, and recommendation systems. The resulting models offer more accurate representations and improved computational efficiency for various applications.