Principal Component Analysis: Dimension Reduction

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Related lectures (29)

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Principal Component Analysis: Olympic Medals & Image Compression

Explores PCA for predicting medals distribution and compressing face images.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Principal Components: Properties & Applications

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

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Linear Dimensionality Reduction: PCA and LDA

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

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Dimensionality Reduction

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