Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
