2 research outputs found
A Comparison of Relaxations of Multiset Cannonical Correlation Analysis and Applications
Canonical correlation analysis is a statistical technique that is used to
find relations between two sets of variables. An important extension in pattern
analysis is to consider more than two sets of variables. This problem can be
expressed as a quadratically constrained quadratic program (QCQP), commonly
referred to Multi-set Canonical Correlation Analysis (MCCA). This is a
non-convex problem and so greedy algorithms converge to local optima without
any guarantees on global optimality. In this paper, we show that despite being
highly structured, finding the optimal solution is NP-Hard. This motivates our
relaxation of the QCQP to a semidefinite program (SDP). The SDP is convex, can
be solved reasonably efficiently and comes with both absolute and
output-sensitive approximation quality. In addition to theoretical guarantees,
we do an extensive comparison of the QCQP method and the SDP relaxation on a
variety of synthetic and real world data. Finally, we present two useful
extensions: we incorporate kernel methods and computing multiple sets of
canonical vectors