Latent Dimension

Latent dimension research focuses on identifying and utilizing lower-dimensional representations of high-dimensional data, aiming to capture underlying structure and reduce computational complexity. Current research emphasizes developing methods to automatically determine optimal latent dimensionality, employing techniques like variational autoencoders (VAEs), autoencoders with various regularization schemes (e.g., least volume, moment pooling), and Gaussian process latent variable models, often incorporating intrinsic dimensionality estimation. This work is significant for improving the efficiency and interpretability of machine learning models across diverse fields, including image generation, personality assessment, anomaly detection, and reduced-order modeling of complex systems.