Towards theory of generic Principal Component Analysis
Journal of Multivariate Analysis
Generic weighted filtering of stochastic signals
IEEE Transactions on Signal Processing
A novel subspace identification approach with enforced causal models
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
A number of signal processing and system identification problems include linear regressions with a reduced-rank regression matrix. A typical step in "subspace-based" algorithms is to apply the singular value decomposition (SVD) to compute a low-rank factorization. However, it is not clear how certain weighting matrices should be defined for best possible accuracy. We present a statistical analysis of the estimate of the reduced-rank regression matrix, and we discuss a couple of approaches for finding weighting matrices