Hello all,
I need some theoretical pointers (formulas, articles, online links) on how to merge Singular Value Decompositions (SVD) of two matrices (two different sets of observations over the same set of features).
That is, I have two SVDs: $A=U_A*S_A*V^T_A$ and $B=U_B*S_B*V^T_B$ and want to know SVD $A|B=U_{A|B}*S_{A|B}*V_{A|B}$. The original matrices $A$ and $B$ are unavailable, the solution must make use of the $U_A, S_A, V_A, U_B, S_B, V_B$ matrices only.
I need this because I want to implement a distributed version of incremental SVD: have several computation nodes work on different sets of observations independently, and then merge their results into one.
Cheers!
Best Answer
The people aspiring for the Netflix prize like incremental SVDs. See
Have you tried searching the ACM digital library for parallel SVD or singular value decomposition?
EDIT1 based on new input : See the following two papers by Hall, Marshall and Martin