svd
people.eecs.berkeley.edu · 1,720 words · saved by 1 readers
N/A
The Singular Value Decomposition; Clustering 127 21 The Singular Value Decomposition; Clustering THE SINGULAR VALUE DECOMPOSITION (SVD) [and its Application to PCA] Problems with PCA: Computing X > X takes ⇥(nd2 ) time. X > X is poorly conditioned ! numerically inaccurate eigenvectors. [The SVD improves both these problems.] [Earlier this semester, we talked about the eigendecomposition of a square, symmetric matrix. Unfortu- nately, nonsymmetric matrices don’t have nice…
related reading
- Singular value decomposition - Wikipediaen.wikipedia.org
- dimensionality reduction - Relationship between SVD and PCA. How to use SVD to perform PCA? - Cross Validatedstats.stackexchange.com
- Singular Value Decomposition as Simply as Possiblegregorygundersen.com
- pca - What is the intuition behind SVD? - Cross Validatedstats.stackexchange.com
- s4.pdfpeople.maths.ox.ac.uk
- Six (and a half) intuitions for SVD — LessWronglesswrong.com
- Matrix Singular Value Decomposition (SVD) Using the Jacobi Algorithm from Scratch JavaScript - James D. McCaffreyJames D. McCaffreyjamesmccaffrey.wordpress.com
- https://www.deeplearningbook.org/contents/linear_algebra.htmldeeplearningbook.org
- MathofSNLnotes2025.pdfpeople.math.ethz.ch
- Principal component analysis - Wikipediaen.wikipedia.org
- Aman's AI Journal • CS229 • Principal Component Analysisaman.ai
- matrixcookbook.pdfmath.uwaterloo.ca