Tucker Decomposition: Multilinear rank and applications in data compression

Related lectures (56)

Covers orthogonal bases, Gram-Schmidt method, linear independence, and orthonormal matrices in vector spaces.

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Explores matrix similarity, eigenvalues, and diagonalization in linear algebra.

Explores linear elasticity in 3D, stress-strain relationship, isotropic materials, and shear stress importance.

Explores the advantages of Decomposition Storage Model with column-oriented storage and various compression techniques.

Covers static equilibrium modeling, focusing on forces, bars, and their relationships in structures.

Covers the concept of polynomial characteristic of a matrix and its relation to eigenvalues.

Covers Lorentz transformations, Maxwell equations, and charge conservation in Lorentz invariant physics.

Covers notations, operations, isomorphism, and polar decomposition of complex numbers.

Explores indexing techniques, inverted files, map-reduce models, and trie usage for efficient information retrieval.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Covers the decomposition of vectors into scalar and vector products.

Covers the analysis of rigid structures and strategies to solve isostatic trusses.

Explores linear algebra concepts through examples and theorems, focusing on matrices and their operations.

Explores the implementation and analysis of elastic energy in hyperelastic materials, including strain and stress tensors.

Covers 3D linear elasticity, stress, strain, beams' behavior under loads, and torsion.

Page 3 of 3