Adaptive Latent Factor

Adaptive latent factor models aim to extract underlying, hidden factors influencing complex datasets, dynamically adjusting to changing data patterns. Current research emphasizes improving model efficiency and accuracy through techniques like hierarchical variational autoencoders (VAEs), nonlinear PID controllers coupled with particle swarm optimization, and deep partial least squares (DPLS) to handle high-dimensional and incomplete data, particularly in finance and educational assessment. These advancements enhance the ability to predict outcomes and uncover non-linear relationships within data, leading to improved forecasting and a deeper understanding of complex systems.