Spectral Clustering: Theory and Applications

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Related lectures (29)

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Statistical Signal Processing

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Cheeger's Inequality

Explores Cheeger's inequality and its implications in graph theory.

Network clustering

Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.

Kernel PCA: Nonlinear Dimensionality Reduction

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

Quantum Chemistry

Covers eigenvalues, eigenfunctions, Hermitian operators, and the measurement of observables in quantum chemistry.

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Eigenvalues and Eigenvectors Decomposition

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Statistical analysis of network data

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Matrix to Density Operator

Explains the transformation from a matrix to the density operator in quantum physics.