Spectral Clustering and Laplacian Eigenmaps

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

Covers unsupervised learning for movie recommendation using singular value decomposition.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

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

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

Explores spectral and singular value decompositions of matrices.

Delves into singular value decomposition and its applications in linear algebra.

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

Explores latent factor analysis for movie genre classification based on male versus female leads.