Handbook of Blind Source Separation: Independent Component Analysis and Applications

By Pierre Comon, Christian Jutten. 2010

1,755 citations recorded by the source corpus.

Subfields: Statistics and Probability, Signal Processing

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Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “Estimation of a wide range of models is possible within the moment matching framework: ICA (e.g., Cardoso & Comon, 1996; Comon & Jutten, 2010), mixtures of Gaus-sians (e.g., Arora & Kannan, 2005; Hsu & Kakade, 2013), latent Dirichlet allocation and topic models (Arora et al., 2012; 2013; Anandkumar…”
Rethinking LDA: Moment Matching for Discrete ICA (2015, neutral): “, [17, 18, 19, 20, 21, 5] and references therein) and machine learning (see [3, 4] and references therein) literature.”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, background): “The problem of “ optimal ” factor extraction under various conditions, however, is its own ﬁ eld of research treated in an extensive existing body on linear decomposition methods and their underlying assumptions (for some overview see Comon & Jutten, 2010; Golub & Van Loan, 1996; Haufe, 2011).”
Looking to listen at the cocktail party (2018, neutral): “available datasets for training and evaluating audio-only speech enhancement. And although there is extensive literature on “blind source separation” for audio-only speech enhancement and separation [Comon and Jutten 2010], most of these techniques require multiple audio channels (multiple microphon”
Tensor decompositions for learning latent variable models (2012, methodology): “Tensor decompositions have been used in signal processing and computational neuroscience for blind source separation and independent component analysis (ICA) [CJ10].”
The Sound of Pixels (2018, background): “While auditory scene analysis [1] is widely studied in the fields of environmental sound recognition [2,3] and source separation [4,5,6,7,8,9], the natural synchronization between vision and sound can provide a rich supervisory signal for grounding sounds in vision [10,11,12].”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, methodology): “The signiﬁcant interest in tensors and their fast emerging applications are reﬂected in books [7,8,12,19–21] and tutorial papers [22–29] covering various aspects of multiway analysis.”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016, positive): “The component analysis (CA) framework usually refers to the application of constrained matrix factorization techniques in order to extract components with specific properties from observed mixed signals and/or estimate the mixing matrix from observed mixed signals [53, 58, 70].”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, methodology): “achieve uniqueness, improve numerical robustness and enhance interpretability are, for example, non-negativity, sparsity, and smoothness [63].”
Tensor Decomposition for Signal Processing and Machine Learning (2016, methodology): “These two approaches are second-order variants of Independent Component Analysis [91].”
The Hitchhiker Guide to: Secant Varieties and Tensor Decomposition (2018, background): “As specific examples, we can quote the complexity of matrix multiplication [12], the P versus NP complexity problem [13], the study of entanglement in quantum physics [15, 14], matchgates in computer science [13], the study of phylogenetic invariants [16], independent component analysis [17], blind ”
Equalization matching of speech recordings in real-world environments (2016, background): “Source separation is an active field of audio research [11, 12, 13, 14, 15].”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “In fact, leading researchers in the field have been using ICA and improved variations for the last 15 years—for example Vigário [21, 44, 159, 160], Hÿvarinen [161, 162], Oja [21, 159, 161, 162], James [20, 39, 55, 108], Makeig [43, 52, 97, 102], Jung [43, 52, 97], Comon [41] and Cichocki [83, 163], ”
Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings (2013, methodology): “BSS methods are especially popular in the statistical signal processing and machine learning areas, where the term blind emphasizes that very little information about the sources or the mixing process is known a priori [5].”
Tensors : A brief introduction (2014, neutral): “…is block diagonal instead of diagonal [26] [79]; (viii) compute a Joint Approximate Diagonalization (JAD) of matrix slices, which may be viewed as another decomposition where the core tensor is not diagonal [62], [87], [89], [2], [86], [51], [20], [30], [56], [69], [14], as depicted in Figure 1.”
Deep neural network based instrument extraction from music (2015, positive): “From x(n) we want to extract an estimate ŝ(n) of the target instrument s(n) and, therefore, we can see instrument extraction as a special case of the general blind source separation (BSS) problem [1, 2].”