I performed both a SVD decomposition and a multidimensional scaling of a 6-dimensional data matrix, in order to get a better understanding of the structure of the data.
Unfortunately, all the singular values are of the same order, implying that the dimensionality of the data is indeed 6. However, I would like to be able to interpret the values of the singular vectors. For instance, the first one seems to be more or less equal in each dimension (ie
(1,1,1,1,1,1)), and the second also has an interesting structure (something like
How could I interpret these vectors? Could you point me to some literature on the subject?