One may consider an analogy to the particular case of matrices (2D tensors), where the usual SVD algorithm also finds two (orthonormal) bases-one for the columns, and the other for the rows-; now the singular values of the matrix are analogous to its Haar coefficients in Questionnaires, and the numerical compressibility of the matrix in these bases is embodied in its (few) largest singular values. The main numerical issue is that the orthogonal transformations of the SVD could become quite complicated: an m × n matrix built out of independent Gaussian noise vectors would have maximal complexity O(min(mn 2 , m 2 n)).