THE THREE EASY ROUTES TO INDEPENDENT COMPONENT ANALYSIS; CONTRASTS AND GEOMETRY

By Jean-François Cardoso. 2001

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The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019, background): “nonstationarity or time correlation in time series settings [6, 37]); and (ii) those restricting the functional form of the mixing functions (e.”
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning (2018, background): “This is remarkable since these principles are well-known in the linear ICA literature, but they have been considered as two distinct principles (Cardoso, 2001; Hyv¨arinen et al., 2001).”
A General Flexible Framework for the Handling of Prior Information in Audio Source Separation (2012, methodology): “[5] C. Févotte and J.-F. Cardoso, “Maximum likelihood approach for blind audio source separation using time-frequency Gaussian source models,” in Proc.”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “These properties can be reformulated algebraically as a symmetric joint diagonalization (JD) of several matrices [81], [83], i.”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “, sJ ), which is an informationtheoretic measure of dependency between random variables [175].”
Dependence, Correlation and Gaussianity in Independent Component Analysis (2003, background): “In particular, similar to the key relation (23), mutual information appears as the correlation minus the sum of marginal 'non-properties': divergence from spectral whiteness or from stationarity of the source processes (see Cardoso, 2001).”