Principal component analysis

Applied sciences
Information engineering
Data science
Dimensionality reduction

Related lectures (30)

About
Privacy
Disclaimer

Graph Chatbot

Page 2 of 3

Multivariate Methods II: ICA for Functional Brain Imaging

Explores Independent Component Analysis in functional brain imaging, focusing on non-Gaussian sources, cleaning artefacts, and group studies.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.

Data Representation: PCA

Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Financial Time Series: ARCH and GARCH Models

Covers regression analysis, multivariate linear regression, principal component analysis, and factor models.

Principal Component Analysis: Geometric Interpretation and Dimension Reduction

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Linear Dimensionality Reduction: PCA and LDA

Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.

Estimating the Term Structure: Principal Component Analysis

Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.

Genes Mirror Geography within Europe

Covers the analysis of principal components using matrices to approximate genetic data.