Principal Component Analysis: Understanding Data Structure

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Related lectures (31)

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Principal Components: Properties & Applications

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

Linear Dimensionality Reduction

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Principal Component Analysis: Properties and Applications

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

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.

Principal Component Analysis: Olympic Medals & Image Compression

Explores PCA for predicting medals distribution and compressing face images.

Dimensionality Reduction

Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.

Principal Component Analysis: Theory and Applications

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.