Singular Value Decomposition

Singular Value Decomposition
This page is under construction.

Reduced Singular Value Decomposition
contains mostly 0s, and this suggests that there should exist another version of SVD with most of the 0s discarded. It is indeed the case, and the "reduced" Singular Value Decompostion is as follows :
or : ...

Singular Value Decomposition. An efficient algorithm for optimizing a linear model. See also, pseudo-inverse.

Spectral methods of learning mixture models are based on the use of Singular Value Decomposition of a matrix which contains data points. The idea is to consider the top k singular vectors, where k is the number of distributions to be learned.

Another data transformation that comes up is singular value decomposition.

poly characteristic polynomial
norm norm of matrix (1-norm, 2-norm, ∞ -norm)
cond condition number in the 2-norm
lu LU factorization
qr QR factorization
chol Cholesky decomposition
svd singular value decomposition ...

See also: Distribution, Classification, Regression, Data mining, Variance