A fast mixing matrix estimation method in the wavelet domain
Signal Processing
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To the underdetermined sparse component analysis (SCA) model with noise, a new robust clustering algorithm based on normalized signal for mixture matrix estimation is addressed in this paper. This approach consists of two parts: signal clustering and matrix recovery. In the first step, according to the feature of normal signals clustering intensively on the unit observed signal hyper-sphere, we propose a criterion to detect and cluster dense observed signal sets, which is the conclusion of deduction from a fit mathematical statistics model. To the second stage for estimating the mixture matrix, Principal Component Analysis is introduced to process dense signal sets. Experiment simulations illustrate that new clustering algorithm's performance on determination of the source numbers and precision of mixing matrix recovery.