Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Algorithms on Subspaces: Revisiting Missing Data Problem in Low-Rank Matrix
International Journal of Computer Vision
Stochastic MV-PURE estimator: robust reduced-rank estimator for stochastic linear model
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
EURASIP Journal on Wireless Communications and Networking
Hessian Matrix vs. Gauss-Newton Hessian Matrix
SIAM Journal on Numerical Analysis
Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems
Wireless Personal Communications: An International Journal
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This paper provides a unified view of, and a further insight into, a class of optimal reduced-rank estimators and filters. An alternating power (AP) method for computing the optimal reduced-rank estimators and filters is derived and analyzed. The AP method is a generalization of the conventional power method for subspace computation, which is shown to be globally and exponentially convergent under weak conditions. When the rank reduction is relatively large, the AP method is computationally more efficient than the conventional methods. The AP method is useful for adaptive computation of the canonical components of a desired reduced-rank estimate, which in turn facilitates the detection of a time-varying rank. The study shown in this paper is particularly useful for applications that involve a large number of sources and a large number of receivers, where rank reduction is either inherent in the multivariate system or required to reduce the model complexity and/or the computational load