Lecture
This lecture covers multivariate methods such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), and Independent Component Analysis (ICA). It explains the rationale for dimensionality reduction, the mathematical principles behind PCA, the relationship between PCA and SVD, and the kernel method for PCA. Additionally, it delves into PLS correlation, providing examples of how these methods can be applied to functional brain imaging data to extract meaningful insights.