u k l is the l mode projection vector of the projection tensor k.
First, in an orthogonal sub-space, the vector of higher projection is selected and becomes the new starting vector.
If S w is a nonsingular matrix, when the column vectors of the projection matrix W = PCA(S w −1S b ), the ratio is maximized.
The basic idea of proCMF is the coefficient of each data point z i (i = 1, 2, …, M) that lies within the subspace spanned by the column vectors of one projection matrix.
The advantage of using this ratio is that it has been proven [26] that if SW is a non-singular matrix, then this ratio is maximized when the column vectors of the projection matrix W are the eigenvectors of S W − 1 S B. The W with dimension C − 1 projects the training data onto a new space called fisherfaces.
First, we add AFlen/2 zeros to left and right sides of projection vector, to generate the zero padded projection vector (ZPPV), with a length of ZPlen = PVlen + AFlen.
For each image, while LDA calculated one kind of projection vector as shown in Figure 5(a), Multifactor Discriminant Analysis obtained individual projection vectors for subjects, viewpoint and lighting.
Secondly, the specific SAR image patch is characterized by a set of projection vectors that are obtained by projecting the SAR image patch onto each weak decision space spanned by each weak classifier.
In smoothing procedure, we need to divide the summation of projection vector data which are overlap with smoothing filter to the length of the smoothing filter, AFlen.
Proof is thoroughly provided that the stationary MC process is linear to first order approximation and that it transforms into one-dimensional autoregressive processes of order one (AR(1)) via the automated choice of projection vectors.
