Dimensionality Reduction: Introduction and Motivation

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Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

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

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

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

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

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

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

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

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.