Dimensionality Reduction

Related lectures (28)

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

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

Covers unsupervised learning for movie recommendation using singular value decomposition.

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

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

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.