Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation

By Arie Yeredor. IEEE Transactions on Signal Processing, 2002

444 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Signal Processing

View Paper: https://www.semanticscholar.org/paper/feb3143b8389165f6d5a570d4c0769acd54fe7cb

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In Analytical Chemistry

In Signal Processing

Citations in the corpus

Measure Transformed Independent Component Analysis (2013, methodology): “…MTICA the separation matrix is estimated via approximate joint diagonalization (Flury and Gautschi, 1986; Cardoso and Souloumiac, 1996; Pham, 2001; Yeredor, 2002; Ziehe et al., 2004; Vollgraf and Obermayer, 2006; Fadaili et al., 2007; Li and Zhang, 2007; Tichavský and Yeredor, 2009; Todros and…”
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram (2006, methodology): “The comparison with temporal decorrelation-based ICA techniques (TDSEP [15], ac-dc [16], SOBI [11], etc.”
Tensor Decomposition for Signal Processing and Machine Learning (2016, methodology): “Finally, using multilinear models such as tensor trains as “universal approximants” has been gaining traction and will continue to grow in the foreseeable future, as a way to get away from the “curse of dimensionality” [56], [108]–[116], [144]–[158].”
A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization (2006, methodology): “The matrix W can be determined from this simultaneous matrix decomposition by means of the algorithms presented in [6, 9, 16, 19, 34, 42, 43, 44 ].”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, background): “…of vector a traceA trace of matrix A detA determinant of matrix A meana arithmetic average of component of vector a š(ν) Fourier transform of process s(t) ⊗ Kronecker product between matrices ⊗ tensor product •j contraction over index j krank{A} Kruskal’s k-rank of matrix A Contents Glossary 3”
Tensor Factorization via Matrix Factorization (2015, unknown): “There are a number of objective functions for finding V [25, 13, 26], but in this paper, we focus on a popular one that penalizes offdiagonal terms:”
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures (2007, methodology): “For algorithms for this type of simultaneous diagonalization we refer to [20], [40], [ 42 ], and the references therein.”