Latent Factor

Latent factor analysis aims to uncover hidden underlying structures or factors that explain observed data patterns across diverse fields, from recommender systems to neuroscience. Current research emphasizes developing robust and efficient algorithms, including those based on variational autoencoders, neural networks, and matrix factorization, to extract these latent factors, often focusing on handling high-dimensional, incomplete data and disentangling shared and private factors across multiple data views. These advancements improve model accuracy, interpretability, and efficiency in applications ranging from personalized recommendations and air pollution prediction to understanding brain activity and psychopathology. The resulting insights contribute to a deeper understanding of complex systems and enable more effective data-driven decision-making.