Sliding window adaptive SVD using the unsymmetric householder partial compressor

Strobach, P.

doi:10.1016/j.sigpro.2009.07.002

Cited by 4 publications

(1 citation statement)

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“…Step (32) can be implemented efficiently by means of sliding window subspace tracking methods, e.g. [21,22]. Also notice that the rows of the unmixing matrix K are normalized in each iteration to remove the scaling ambiguity in the NICA problem.…”

Section: Sliding-window M-nicamentioning

confidence: 99%

Blind separation of non-negative source signals using multiplicative updates and subspace projection

Bertrand

Moonen

2010

Signal Processing

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In this paper, we consider a noise-free blind source separation problem with independent non-negative source signals, also known as non-negative independent component analysis (NICA). We assume that the source signals are well-grounded, which means that they have a non-vanishing pdf in a positive neighborhood of zero. We propose a novel algorithm, referred to as multiplicative NICA (M-NICA), which uses multiplicative updates together with a subspace projection based correction step to reconstruct the original source signals from the observed linear mixtures, and which is based only on second order statistics. A multiplicative update has the facilitating property that it preserves non-negativity, and does not depend on a user-defined learning rate, as opposed to gradient based updates such as in the non-negative PCA (NPCA) algorithm. We provide batch mode simulations of M-NICA and compare its performance to NPCA, for different types of signals. It is observed that M-NICA generally yields a better unmixing accuracy, but converges slower than NPCA. Especially when the amount of data samples is small, M-NICA significantly outperforms NPCA. Furthermore, a sliding window implementation of both algorithms is described and simulated, where M-NICA is observed to provide the best results.

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Section: Sliding-window M-nicamentioning

confidence: 99%