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Nonnegative joint diagonalization by congruence for semi-nonnegative independent component analysis

Abstract : The Joint Diagonalization of a set of matrices by Congruence (JDC) appears in a number of signal processing problems, such as in Independent Component Analysis (ICA). Recent developments in ICA under the nonnegativity constraint of the mixing matrix, known as semi-nonnegative ICA, allow us to obtain a more realistic representation of some real-world phenomena, such as audios, images and biomedical signals. Consequently, during this thesis, the main objective was not only to design and develop semi-nonnegative ICA methods based on novel nonnegative JDC algorithms, but also to illustrate their interest in applications involving Blind Source Separation (BSS). The proposed nonnegative JDC algorithms belong to two fundamental strategies of optimization. The first family containing five algorithms is based on the Jacobi-like optimization. The nonnegativity constraint is imposed by means of a square change of variable, leading to an unconstrained problem. The general idea of the Jacobi-like optimization is to factorize the matrix variable as a product of a sequence of elementary matrices which is defined by only one parameter, then to estimate these elementary matrices one by one in a specific order. The second family containing one algorithm is based on the alternating direction method of multipliers. Such an algorithm is derived by successively minimizing the augmented Lagrangian function of the cost function with respect to the variables and the multipliers. Experimental results on simulated matrices show a better performance of the proposed algorithms in comparison with several classical JDC methods, which do not use the nonnegativity as constraint prior. It appears that our methods can achieve a better estimation accuracy particularly in difficult contexts, for example, for a low signal-to-noise ratio, a small number of input matrices and a high coherence level of matrix. Then we show the interest of our approaches in solving real-life problems. To name a few, we are interested in i) the analysis of the chemical compounds in the magnetic resonance spectroscopy, ii) the identification of the harmonically fixed spectral profiles (such as piano notes) of a piece of signal-channel music record by decomposing its spectrogram, iii) the partial removal of the show-through effect of digital images, where the show-through effect were caused by scanning a semi-transparent paper. These applications demonstrate the validity and improvement of our algorithms, comparing with several state-of-the-art BSS methods.
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Lu Wang. Nonnegative joint diagonalization by congruence for semi-nonnegative independent component analysis. Signal and Image processing. Université Rennes 1, 2014. English. ⟨NNT : 2014REN1S141⟩. ⟨tel-01227456⟩

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