A Unifying Information-Theoretic Framework for Independent Component Analysis

By Te-Won Lee, Mark A. Girolami, Anthony J. Bell, Terrence J. Sejnowski. 2000

429 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/00d1f4def02beaa70edf4ab4a23d1e5cbe2f0aef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. José M. Bioucas-Dias
2. Soo-Young Lee
3. Scott Makeig
4. Arnaud Delorme
5. Seungjin Choi
6. Bernhard Schölkopf
7. Jose M. P. Nascimento
8. Julie Anne Onton

In Artificial Intelligence

1. Bernhard Schölkopf
2. Arthur Gretton
3. Alex Smola
4. Kenji Fukumizu
5. Bharath K. Sriperumbudur
6. Le Song
7. David Lopez-Paz
8. Francis R. Bach

In Signal Processing

1. Scott Makeig
2. Arnaud Delorme
3. Arthur Gretton
4. Bernhard Schölkopf
5. Soo-Young Lee
6. Seungjin Choi
7. Julie Anne Onton
8. Alex Smola

Citations in the corpus

• Imaging brain dynamics using independent component analysis (2001, background): “ICA algorithms and their interrelationships are available [12], [13], as are two edited collections of papers [14], [15] and proceedings from two international workshops (ICA99, ICA2000).”
• Mining event-related brain dynamics. (2004, methodology): “A linear ICA decomposition is one whose component activities are most temporally independent in some sense [ 27 ], and therefore also most temporally ‘distinct’.”
• Kernel Methods for Measuring Independence (2005, methodology): “…of instantaneous independent component analysis(ICA), drawing on the numerous existing surveys of ICA and related methods, including those by Hyvärinen et al. (2001); Lee et al. (2000); Cichocki and Amari (2002); Haykin (1998); as well as the review by Comon (1994) of older literature on the topic.”
• Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “It is worth noting that the infomax algorithm provides almost same formulation as maximum likelihood estimation [120, 24], negentropy maximization [70], Bussgang algorithm [98], and minimizing mutual information [5, 59].”
• Does independent component analysis play a role in unmixing hyperspectral data? (2003, methodology): “While PCA uses only second-order statistics, ICA looks for components which are statistically independent rather than uncorrelated; thus, it requires statistics of orders higher than the second [58].”
• Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “It is worth noting that the infomax algorithm provides almost same formulation as maximum likelihood estimation [120, 24], negentropy maximization [70], Bussgang algorithm [98], and minimizing mutual information [5, 59].”
• Independent EEG Sources Are Dipolar (2012, methodology): “Though ICA algorithms all have the same root goal [27] and generally produce”
• EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. (2004, methodology): “expectancy of a target stimulus (Makeig et al., 1999) and with visual awareness (Rodriguez et al., 1999). The sufficiency of studying average ERPs has also been questioned by Makeig et al. (2002), who showed that some average ERP peaks may result from partial synchronization of oscillatory EEG proce”
• ICA of fMRI Group Study Data (2002, background): “ICA (Cardoso, 1998; Hyvä rinen, 1999b; Lee et al., 2000) can be regarded as a generalization of the more well-known principal components analysis (PCA) (Jolliffe , 1986).”