Live briefing
Everything about FastICA
Machine learning algorithm
FastICA is an efficient and popular algorithm for independent component analysis invented by Aapo Hyvärinen at Helsinki University of Technology. Like most ICA algorithms, FastICA seeks an orthogonal rotation of prewhitened data, through a fixed-point iteration scheme, that maximizes a measure of non-Gaussianity of the rotated components. Non-gaussianity serves as a proxy for statistical independence, which is a very strong condition and requires infinite data to verify. FastICA can also be alternatively derived as an approximative Newton iteration. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: FastICAA 5-minute orientation
- Skim a recent paper: 2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICAInfluential work since 2022
- Read the landmark paper: A Novel Framework Based on FastICA for High Density Surface EMG DecompositionCited 263 times
- Go sideways: Independent component analysisA closely related topic
- Go sideways: Gram–Schmidt processA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A Novel Framework Based on FastICA for High Density Surface EMG Decomposition
Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the CramÉr-Rao Lower Bound
The FastICA Algorithm Revisited: Convergence Analysis
Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network
Foreground removal usingfastica: a showcase of LOFAR-EoR
An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms
All-sky astrophysical component separation with Fast Independent Component Analysis (FASTICA)
Performance analysis of the FastICA algorithm and Crame/spl acute/r-rao bounds for linear independent component analysis
🆕 Influential since 2022
2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA
Adaptive Online Decomposition of Surface EMG Using Progressive FastICA Peel-Off
Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off
A packaged food internal Raman signal separation method based on spatially offset Raman spectroscopy combined with FastICA
SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm
đź“š Books worth reading
Open Library didn't respond; try again in a moment.
đź”— Related topics
- Independent component analysis In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents.…
- Gram–Schmidt process In mathematics, particularly linear algebra and numerical analysis, the Gram–Schmidt process or Gram-Schmidt algorithm is a way of finding a set of two…
- Singular value decomposition In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a scaling, followed…
- Linear subspace In mathematics, and more specifically in linear algebra, a linear subspace or vector subspace is a vector space that is a subset of some larger vector…
- Invertible matrix In linear algebra, an invertible matrix (non-singular, non-degenerate or regular) is a square matrix that has an inverse. In other words, if a matrix is…
- Principal component analysis Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data…
- Numerical linear algebra Numerical linear algebra, sometimes called applied linear algebra, is the study of how matrix operations can be used to create computer algorithms which…
- Gauss–Seidel method In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method…