| Abstract:
A matrix decomposition method for positive semidefinite matrices based
on a given subspace is proposed in this paper. It is shown that any positive
semidefinite matrix can be decomposed uniquely into two positive semidefinite
parts with specified rank, one of them is orthogonal to the subspace. This
method is then compared to rank additivity decomposition and the difference,
as well as the close connection between these two decompositions are given.
Finally, the proposed decomposition method is used to develop a new recursive
parameter estimation algorithm for linear systems. |
