Gaussian Process Factor Analysis

Gaussian Process Factor Analysis (GPFA) is a statistical technique used to uncover low-dimensional latent structures driving high-dimensional data, particularly in time series analysis. Current research focuses on improving the efficiency and scalability of GPFA models, including developing conditionally conjugate versions for tractable inference and employing sparse Gaussian processes to handle large datasets. Applications range from analyzing neural activity in neuroscience to modeling trust and behavior in multi-agent systems, highlighting GPFA's versatility in uncovering underlying patterns in complex data. The development of robust and efficient GPFA algorithms contributes to improved interpretability and anomaly detection in various fields.