Live briefing
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: Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralizationInfluential work since 2022
- Pick up a classic book: Infomax Common Core Math ReadersThe most-reprinted book on the subject
- Read the landmark paper: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian SourcesCited 1,697 times
- Go sideways: FastICAA closely related topic
- Go sideways: Joint Approximation Diagonalization of Eigen-matricesA closely related topic
📖 Definitions
Noun A particular optimization method, prescribing that a function that maps a set of inputs to a set of outputs should maximize the average mutual information between the two sets, subject to constraints.
🏆 Landmark papers (most cited)
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
Infomax and maximum likelihood for blind source separation
Evaluating two-step PCA of ERP data with Geomin, Infomax, Oblimin, Promax, and Varimax rotations
Evaluation of PCA and ICA of simulated ERPs: Promax vs. infomax rotations
An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms
Learning urban region representations with POIs and hierarchical graph infomax
CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis
Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning
🆕 Influential since 2022
Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization
An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal control
Multiview Deep Graph Infomax to Achieve Unsupervised Graph Embedding
SSPool: A Simple Siamese Framework for Graph Infomax Pooling
Domain Adaptive Graph Infomax via Conditional Adversarial Networks
📚 Books worth reading
- 📕Infomax Common Core Math ReadersRosen Publishing Staff · 2015
Neural networksSimon S. Haykin · 1994- 📕What Is a Space Probe? (Infomax)Richards, Charles · 2012
- 📕Meet President Obama (Infomax Common Core Readers)Mel Bartlett · 2012
- 📕Who Was Helen Keller? (Infomax Common Core Readers)Caryl Mcdonald · 2012
- 📕Pierwszy tydzień oswobodzeniaEugeniusz L. Ćwięczek · 2010
Racibórz · 1998
Zawsze obcyHenryk Tomanek · 2014
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…