Multilinear subspace learning

Related lectures (27)

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Introduces eigenspaces in linear algebra through definitions and practical examples of determining eigenspaces for matrices.

Introduces the concept of a submanifold in a linear space, defining it as a set smoothly embedded in the space.

Explains orthogonal projection in linear algebra, focusing on transforming non-orthogonal bases into orthogonal ones.

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

Covers strategies for matrix operations and the concept of vector spaces.

Continues the discussion on Simon's hidden subgroup problem, focusing on finding a basis.

Discusses the transition from Turning Point to SpeakUp for interactive activities in the course.

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

Explores the orthogonal subspace in a vector space, demonstrating its properties and dimension determination.

Introduces the Gram-Schmidt orthogonalization process to find orthogonal bases for vector subspaces.