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Everything about Independent component analysis
Signal processing computational method
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA was invented by Jeanny Hérault and Christian Jutten in 1985. ICA is a special case of blind source separation. A common example application of ICA is the "cocktail party problem" of listening in on one person's speech in a noisy room. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Independent component analysisA 5-minute orientation
- Skim a recent paper: Optimal Portfolio Diversification via Independent Component AnalysisInfluential work since 2022
- Pick up a classic book: Independent Component AnalysisThe most-reprinted book on the subject
- Read the landmark paper: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysisCited 23,985 times
- Go sideways: FastICAA closely related topic
- Go sideways: Joint Approximation Diagonalization of Eigen-matricesA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
Independent component analysis: algorithms and applications
Face recognition by independent component analysis
Independent component analysis for biomedical signals
Independent component analysis: an introduction
Topographic Independent Component Analysis
Independent component analysis: recent advances
Unmixing fMRI with independent component analysis
🆕 Influential since 2022
Optimal Portfolio Diversification via Independent Component Analysis
Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning
Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction
How does Independent Component Analysis Preprocessing Affect EEG Microstates?
Prediction of electrical energy consumption using principal component analysis and independent components analysis
📚 Books worth reading
Independent Component AnalysisJames V. Stone · 2004
Independent component analysisStephen Roberts · 2001
Independent component analysisAapo Hyvarinen · 2001
Wavelet and independent component analysis applications IXHarold H. Szu · 2002
Independent Component Analysis and Blind Signal SeparationJose C. Principe, Simon Haykin · 2006
Independent Component AnalysisAddisson Salazar, Luis Vergara · 2018
Independent component analysisTe-Won Lee · 1998
Advances in Independent Component Analysis (Perspectives in Neural Computing)Mark Girolami · 2000
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- FastICA FastICA is an efficient and popular algorithm for independent component analysis invented by Aapo Hyvärinen at Helsinki University of Technology. Like…
- Joint Approximation Diagonalization of Eigen-matrices Joint Approximation Diagonalization of Eigen-matrices (JADE) is an algorithm for independent component analysis that separates observed mixed signals into…
- Signal separation Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals,…
- Mixture model In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring…
- Principal component analysis Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data…
- Infomax Infomax', or the principle of maximum information preservation, is an optimization principle for artificial neural networks and other information processing…
- Multivariate normal distribution In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization…
- Expectation–maximization algorithm In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates…