Sensitivity analysis for the problem of matrix joint diagonalization. (English) Zbl 1184.15010
Author’s summary: We investigate the sensitivity of the problem of nonorthogonal (matrix) joint diagonalization (NOJD). First, we consider the uniqueness conditions for the problem of exact joint diagonalization (EJD), which is closely related to the issue of uniqueness in tensor decompositions. As a byproduct, we derive the well-known identifiability conditions for independent component analysis (ICA) based on an EJD formulation of ICA.
We next introduce some known cost functions for NOJD and derive flows based on these cost functions for NOJD. Then we define and investigate the noise sensitivity of the stationary points of these flows. We show that the condition number of the joint diagonalizer and uniqueness of the joint diagonalizer as measured by modulus of uniqueness (as defined in this paper) affect the sensitivity. We also investigate the effect of the number of matrices on the sensitivity. Our numerical experiments confirm the theoretical results.While this paper was under review a few of its results were presented in the ICASSP07 conference in Honolulu, HI .
We next introduce some known cost functions for NOJD and derive flows based on these cost functions for NOJD. Then we define and investigate the noise sensitivity of the stationary points of these flows. We show that the condition number of the joint diagonalizer and uniqueness of the joint diagonalizer as measured by modulus of uniqueness (as defined in this paper) affect the sensitivity. We also investigate the effect of the number of matrices on the sensitivity. Our numerical experiments confirm the theoretical results.While this paper was under review a few of its results were presented in the ICASSP07 conference in Honolulu, HI .
Reviewer: Witold Więsław (Wrocław)
MSC:
15A21 | Canonical forms, reductions, classification |
15A69 | Multilinear algebra, tensor calculus |
15B52 | Random matrices (algebraic aspects) |
62H25 | Factor analysis and principal components; correspondence analysis |