Eigendecomposition
A linear algebra method that decomposes a matrix into eigenvectors and eigenvalues. It is the basis of dimensionality reduction techniques such as Principal Component Analysis (PCA).
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Topics covered by the video series include vector spaces, inner products, matrix operations, subspaces, orthogonality, QR decomposition, LU decomposition, eigendecomposition, symmetric matrices and singular value decomposition (SVD).
Eigendecomposition is closely related to principal component analysis (PCA) and is the basis of dimensionality reduction.
A spectral basis is a set of orthogonal basis vectors obtained through eigendecomposition, where the magnitude of the eigenvalues reflects the information content or importance of the corresponding direction.
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Topics covered by the video series include vector spaces, inner products, matrix operations, subspaces, orthogonality, QR decomposition, LU decomposition, eigendecomposition, symmetric matrices and singular value decomposition (SVD).
50%UnverifiedEigendecomposition is closely related to principal component analysis (PCA) and is the basis of dimensionality reduction.
50%UnverifiedA spectral basis is a set of orthogonal basis vectors obtained through eigendecomposition, where the magnitude of the eigenvalues reflects the information content or importance of the corresponding direction.
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