Lecture
Singular Value Decomposition
Related lectures (170)
Principal Component Analysis: Dimensionality Reduction
Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.
Linear systems resolution
Covers the resolution of linear systems and its link to optimization problems.
Spectral Decomposition
Explores spectral and singular value decompositions of matrices.
Data Driven Science: MODNet Methodology
Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.
Dimensionality Reduction
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Principal Component Analysis: Olympic Medals & Image Compression
Explores PCA for predicting medals distribution and compressing face images.
Linear Discriminant Analysis: Generative Methods
Covers Linear Discriminant Analysis (LDA) as a generative method for classification.
Multivariate Methods I
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Generalised Additive Models: Tackling Dimensionality Issue
Explores Generalised Additive Models and methods to tackle dimensionality issues efficiently.
Balanced Realization: SISO Case
Covers the concept of balanced realization in the SISO case, focusing on system observability and controllability.