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What does Singular Value Decomposition exactly do?
I have a question regarding the dimension reduction technique called Singular Value Decomposition in Rapidminer. I am using it in the context of textmining and i want to know what it actually does. I have searched everywhere for an answer including this forum but i couldn't find any.
I did some experiments to find out what SVD does and to my experience it decomposes the (term versus document )matrix in three matrices: USV*. Then it replaces the original (term versus document )matrix with the matrix U and applies the dimension reduction on this matrix. Is this correct and if so, why is the orginal matrix replaced with the matrix U. Is there some explanation or theory behind this?
I hope that you can help me out with this. Maybe there is some file that describes the working of SVD in Rapidminer, if there is such an file, maybe you can pass me a link to it.
Thanx in advance.