MSVD Singular value decomposition for polarization analysis. MSVD computes the singular value decomposition for multiple- transform polarization analysis at all or selected frequencies. [D,U1,V1]=MSVD(W) computes the singular value decomposition of sub-matrices of the eigentransform W. The input matrix W is an J x N x K matrix where J is the number of transforms (frequencies or scales) N is the number of dataset components K is the number of eigentransforms (tapers or wavelets) The output is D -- J x MIN(K,N) matrix of singular values U1 -- J x N matrix of first left singular vectors V1 -- J x K matrix of first right singular vectors The left singular vectors U1 are the eigenvectors of the spectral matrix, while the right singular vectors V1 are the eigenvectors of the "structure matrix". W may optionally be of size M x J x N x K, in which case the output arguments are all three-dimensional matrices with sizes D -- M x J x MIN(K,N) matrix of singular values U1 -- M x J x N matrix of first left singular vectors V1 -- M x J x K matrix of first right singular vectors [D,U1,V1]=MSVD(W,INDEX) for three-dimensional W optionally performs the SVD only at the rows of MMAT indicated by INDEX. The output arguments then all have LENGTH(INDEX) rows rather than M. [D,U1,V1,TR]=MSVD optionally outputs TR, the trace of the spectral matrix, which is of size J (for 3-D W) or M x J (for 4-D W). MSVD(..., 'quiet') suppresses a progress report. Usage: [d,u1,v1]=msvd(w); [d,u1,v1]=msvd(w,index); [d,u1,v1,tr]=msvd(w,index); 'msvd --t' runs a test. _________________________________________________________________ This is part of JLAB --- type 'help jlab' for more information (C) 1993--2014 J.M. Lilly --- type 'help jlab_license' for details