Singular value decomposition (SVD) is one of the most useful matrix decompositions in linear algebra. Here, a novel application SVD recovering ripped photos was exploited. Recovery done by applying truncated iteratively. Performance evaluated using Frobenius norm. Results from few experimental were decent.

The canonical variates can be calculated from the eigenvectors of the within-group sums of squares and cross-products matrix. However, G03ACF calculates the canonical variates by means of a singular value decomposition (SVD) of a matrix V . Let the data matrix with variable (column) means subtracted be X and let its rank be k; then the k by (ng 1) matrix V is given by: V 1⁄4 QXQg; where Qg is a...