Eigenvalue decomposition
Type: Concept
As of November 2026, Eigenvalue decomposition has been mentioned in one podcast episode on ICLR (May 2015). Used to create an explicitly low dimensional representation of the image.
In linear algebra, eigendecomposition is a factorization of a matrix into a canonical form given by , where is a diagonal matrix containing the eigenvalues of on the diagonal, and is a matrix whose columns are the corresponding eigenvectors of . Only diagonalizable matrices can be factorized in this way. When the matrix being factorized is a normal or real symmetric matrix, the decomposition is called "spectral decomposition", derived from the spectral theorem.
| Mentioned in | 1 episode on 1 podcast (as of November 2026) |
|---|---|
| First heard | May 2015 on ICLR |
| Field | Mathematics |
Who talks about Eigenvalue decomposition on podcasts?
Eigenvalue decomposition came up; the most recent mention was May 2015 on ICLR.
- ICLR2015-olivier-henaff on ICLR (May 2015)
What is Eigenvalue decomposition mentioned alongside?
Eigenvalue decomposition is most often mentioned alongside Oriented band pass filters, Visual system and Gradient Descent.
Mentioned together: Oriented band pass filters, Visual system, Gradient Descent, Gaussian scale mixture, Hierarchical representation, Unsupervised learning problem, Natural Images, Fourier space, Synthesis procedure, Wavelet decomposition