Independent Component Analysis

By Aapo Hyvärinen, Juha Karhunen, Erkki Oja. 2001

10,232 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, positive): “The associated eigenvectors accurately recover both ~ D 1 and ~ D 2 , after a final step of Independent Component Analysis (ICA) [7, 53, 54] that successfully disentangles the contributions coming from the two constraints (see S1 Appendix).”
Time-domain blind audio source separation using advanced ICA methods (2007, neutral): “Here, we address the blind separation of audio sources by means of Independent Component Analysis (ICA) [3], which is a popular method for BSS using the assumption that the original sources are mutually independent.”
Products of Experts (2007, neutral): “4 Relation to Independent and Extreme Components Analysis NoiselessIndependent Components Analysis(ICA) [3] with an equal number of input dimensions and source distributions can be written as a PoE model as follows,”
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow (2007, neutral): “It is also interesting to point out the similarity between the problem of image segmentation by means of active contours and the problem of blind source separation [as a speciﬁc instance of independent component analysis (ICA) [59]].”
Encounters with neighbours : current developments of concepts based on recurrence plots and their applications (2003, neutral): “rence phenomena in cosmic-ray intensity, Monk and Compton, 1939). Eckmann et al. (1987) have introduced a tool which can visualize the recurrence of states ~xi in a phase space.”
Density estimation using Real NVP (2016, background): “Also, this ﬂexibility will help us leverage recent advances in batch normalization [25] and residual networks [19, 20].”
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, methodology): “, using principal component analysis (PCA) [96], independent component analysis (ICA) [97], and Laplacian Eigenmaps [98] and other more advanced techniques, are commonly and conventionally used approaches to decipher AI models by representing the most important features.”
Estimating mutual information. (2003, neutral): “Expressions obtained by entropy maximalization using averages of some functions of the sample data as constraints [4] are more robust, but are still very crude approximations.”
Independent Component Analysis via Distance Covariance (2013, neutral): “Hyv¨arinen et al. (2001) provide an extensive overview including discussion of non-Gaussianity, some algo-1 rithms for estimating ICs, and applications in blind source separation, feature extraction, compression and redundancy reduction, medical signal processing (fMRI, ECG, EEG), clustering, and…”
Invariant co‐ordinate selection (2009, neutral): “The relationship between projection pursuit itself and ICA is well documented; see for example Hyvârinen et al (2001).”
Validating the independent components of neuroimaging time series via clustering and visualization (2004, neutral): “Independent component analysis (ICA) (Hyvärinen et al., 2001b) is a general-purpose statistical model that has been widely used in the analysis of brain imaging data (see, e.g., Makeig et al., 1997; McKeown et al., 1998; Vigário et al., 2000).”
Estimation of Non-Normalized Statistical Models by Score Matching (2005, neutral): “Note that no dimension reduction was performed, and we show filters instead of basis vectors, which is why the results are much less smooth and " beautiful " than some published ICA results (Hyvärinen et al., 2001).”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, neutral): “Independent component analysis (ICA) is a general-purpose statistical model that has been used in many applications, see [7] for a review.”
Automatic Video Classification: A Survey of the Literature (2008, neutral): “ICA is a method for finding a set of statistically independent and non-Gaussian components that produce a set of multivariate data [44].”