Principal Component Analysis: Dimension Reduction

Related lectures (29)

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

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

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Page 2 of 2