Lectures related to Spectral Clustering: Finding Clusters

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.

Covers unsupervised learning for movie recommendation using singular value decomposition.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.