Open Access
Independent Component Analysis.
Seungjin Choi
- pp 435-459
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TLDR
The standardization of the IC model is talked about, and on the basis of n independent copies of x, the aim is to find an estimate of an unmixing matrix Γ such that Γx has independent components.About:
The article was published on 2012-01-01 and is currently open access. It has received 2296 citations till now. The article focuses on the topics: Independent component analysis.read more
Citations
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Journal ArticleDOI
Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
Zbynek Koldovsky,Petr Tichavsky +1 more
TL;DR: In this paper, three gradient-based estimation algorithms are derived using the maximum likelihood principle and compared with the Natural Gradient algorithm for Independent Component Analysis and with One-Unit FastICA based on negentropy maximization.
Journal ArticleDOI
Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior.
David A. Bridwell,James F. Cavanagh,Anne G.E. Collins,Anne G.E. Collins,Michael D. Nunez,Ramesh Srinivasan,Sebastian Stober,Vince D. Calhoun,Vince D. Calhoun +8 more
TL;DR: This article highlights the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP).
Proceedings ArticleDOI
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data
TL;DR: This work proposes a grouping-based interpretable neural network model, GroupINN, that effectively classifies cognitive performance with 85% fewer model parameters than baseline deep models, while also identifying the most predictive brain subnetworks within several task-specific contexts.
Journal ArticleDOI
A Region-Growing Permutation Alignment Approach in Frequency-Domain Blind Source Separation of Speech Mixtures
Lin Wang,Heping Ding,Fuliang Yin +2 more
TL;DR: A new alignment method based on an inter-frequency dependence measure: the powers of separated signals that minimizes the spreading of the misalignment at isolated frequency bins to others, hence to improve permutation alignment.
Optimization Algorithms on Riemannian Manifolds with Applications
TL;DR: In this paper, the authors generalized three well-known unconstrained optimization approaches for Rn to solve optimization problems with constraints that can be viewed as a d-dimensional Riemannian manifold.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
Independent component analysis, a new concept?
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).