Latent Factor Analysis: Movie Genre Classification

Related lectures (27)

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Explores matrix decompositions for solving linear systems and simulating dynamics.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Covers the construction of an iterative method for linear systems by decomposing a matrix A into P, T, and P_A.

Explores the Jordan normal form and its applications in linear algebra, focusing on diagonalization and cyclic bases.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Covers the analytical expression of orthogonal projections and reflections in 2D space.

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

Covers the decomposition of matrices into triangular blocks and spectral decomposition.