Feature value orthogonal decomposition A = PDP ', WHERE P' = Inverse (p), D is Diagonal Matrix of Eigenvalues. Features: A ^ {n} = Pd ^ {n} p '; e ^ {a} = pe ^ {D} P '
QR Decomposing a = QR, WHERE R IS An Upper Triangular Matrix and q IS An Orthogonal Matrix. QR Decomposing is often used in solution linear equations.
LU Decomposes a = Lu, WHERE L IS An Lower Triangular Matrix and U An Upper Triangular Matrix. QR Decomposing is often used in the understanding of AX = B => L (UX) = B => = B, UX = Y.
SVD Decomposition A = UDV ', WHERE A IS MAGONAL Matrix. U is MN MATRIX AND V IS NN Matrix Such That U'U = V'V = i. Not all matrices can be distinguished However, all active matrices can be distinguished.
The SVD is a use of quadratic errors. This meaning is very similar to SVD, PCA, and Fourier. In MutiView, SVD is almost a must-use tool for processing Fundmental Matrix.
Further Reading:
Direct Least Squares Fitting of Ellipses by Fitzgibbonhttp: //www.uwlax.edu/faculty/will/svd/index.htmlInvariant Subspaces and Condition and Singular Value Decomposition A few properties of Singular Value Decomposition
