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Efficient, high performance, subspace tracking for time-domain data

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1 Author(s)
Davila, C.E. ; Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

This paper describes two new algorithms for tracking the subspace spanned by the principal eigenvectors of the correlation matrix associated with time-domain (i.e., time series) data. The algorithms track the d principal N-dimensional eigenvectors of the data covariance matrix with a complexity of O(Nd2), yet they have performance comparable with algorithms having O(N2d) complexity. The computation reduction is achieved by exploiting the shift-invariance property of temporal data covariance matrices. Experiments are used to compare our algorithms with other well-known approaches of similar and/or lower complexity. Our new algorithms are shown to outperform the subset of the general approaches having the same complexity. The new algorithms are useful for applications such as subspace-based speech enhancement and low-rank adaptive filtering.

Published in:

Signal Processing, IEEE Transactions on  (Volume:48 ,  Issue: 12 )