Independent Component Analysis

Also known as ICA

Independent component analysis (ICA) aims to recover hidden signals from mixtures by finding components that are statistically independent, often using their non-Gaussianity; the result is generally ambiguous up to ordering and scale. Linear methods use whitening and contrasts based on kurtosis, likelihood, or optimal transport, while online and federated methods address streaming data and distributed estimates. Nonlinear ICA uses additional structure, such as temporal context or generative models, to make source recovery possible beyond linear mixtures. Applications include EEG and neuroimaging, but a central challenge is determining when sources are identifiable and estimable with limited data, especially in high dimensions or under restrictive distributional assumptions.

Latest papers

23 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Separation Is a Riesz Energy: How Many Environments Does ICA Need?
Nov 4, 2026Yuda Bi, Yubo Guo, Jiaxiao Cai +3Independent Component Analysis
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA · Huazhong University of Science and Technology, Wuhan, China · University of Pennsylvania, Philadelphia, PA, USA +2
Identifying Dependent Components from Multi-domain Linear Mixtures
Oct 20, 2026Danru Xu, Lauri Parkkonen, Sara Magliacane +1Latent Variable Models Independent Component Analysis
University of Amsterdam, Netherlands · Aalto University, Finland · Saarland University, Germany +1
Complete or Sparse: A Tale of Two Identifiabilities
Oct 14, 2026Junze Zhou, David KlindtSparse Coding Representation Identifiability
Cold Spring Harbor Laboratory
Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplace-like Sources
Sep 18, 2026Isaac Manring, Kejun HuangIndependent Component Analysis Representation Identifiability
Department of Computer Science University of Florida Gainesville, FL 32611, USA
MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity
Aug 25, 2026Victor Solomon, Zening Fu, Rafal Angryk +2Dynamic Functional Connectivity Multimodal Fusion
Department of Computer Science, Georgia State University · Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University
Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)
Aug 17, 2026Oktay AgcaogluIndependent Component Analysis Brain Network Analysis
NEFI-Research, LLC
Foundations of Independent Component Analysis
Aug 13, 2026Patrick ForréParameter Identifiability Independent Component Analysis
AI4Science Lab Korteweg-de Vries Institute for Mathematics University of Amsterdam
AMICA-Python: Adaptive Mixture Independent Component Analysis with Anderson Acceleration
Jul 20, 2026Scott Huberty, Christian O'ReillyUnsupervised Learning Independent Component Analysis
University of Southern California, Los Angeles, CA, USA · University of South Carolina, Columbia, SC, USA
Linear Independent Component Analysis via Optimal Transport
Jul 15, 2026Ashutosh Jha, Michel Besserve, Simon BuchholzIndependent Component Analysis
Methods Center, University of Tübingen · Max Planck Institute for Intelligent Systems, Tübingen · Institute of Artificial Intelligence, TU Braunschweig
Non-Gaussianity Reveals Interpretable Directions in Language Models
Jun 10, 2026Sida Liu, Feijiang Han, Sarah Wiegreffe +1Independent Component Analysis LLM Interpretability
Independent Researcher · University of Maryland
Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels
Jun 1, 2026Jose Marie Antonio Miñoza, Rex Gregor Laylo, Sebastian C. IbañezUncertainty Quantification Independent Component Analysis
Center for AI Research · Department of Education · Makati, Philippines
Disentanglement Beyond Generative Models with Riemannian ICA
May 21, 2026Edmond CunninghamDisentangled Representation Learning Independent Component Analysis
University of Massachusetts Amherst
Learnability and Competition in High-Dimensional Multi-Component ICA
May 8, 2026Eser Ilke Genc, Samet Demir, Zafer DoganIndependent Component Analysis Unsupervised Representation Learning
MLIP Research Group, KUIS AI Center, Koç University · Department of EEE, Koç University
Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension
Apr 27, 2026Kamya Hari, Taha Binhuraib, Jin Li +2Independent Component Analysis Neuroimaging
School of Electrical and Computer Engineering, Georgia Institute of Technology · School of Psychological and Brain Sciences, Georgia Institute of Technology · Department of Linguistics, Stanford University
EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
Apr 20, 2026Zikun Zhou, Wenshuo Wang, Wenzhuo Liu +5Independent Component Analysis EEG Decoding
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds
Apr 10, 2026Yuwen JiangQuery Identifiability Independent Component Analysis
School of Artificial Intelligence, Guangzhou Institute of Science and Technology, Guangzhou, China
PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA
Mar 20, 2026Yuan-Hao Wei, Yan-Jie SunIndependent Component Analysis Variational Autoencoders
Hong Kong Polytechnic University
Reservoir Subspace Injection for Online ICA under Top-n Whitening
Mar 2, 2026Wenjun Xiao, Yuda Bi, Vince D CalhounIndependent Component Analysis Reservoir Computing
Department of Computer Science, The George Washington University, Washington, DC 20052 USA · Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303 USA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
Scaling of learning time for high dimensional inputs
Mar 1, 2026Carlos Stein BritoNeural Network Training Dynamics Scaling Laws
NightCity Labs, Lisbon, Portugal
A 1/R Law for Kurtosis Contrast in Balanced Mixtures
Feb 25, 2026Yuda Bi, Wenjun Xiao, Linhao Bai +1Independent Component Analysis
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303 USA · Department of Computer Science, The George Washington University, Washington, DC 20052 USA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
Self-sufficient Independent Component Analysis for Demixing Flows
Nov 29, 2025Song LiuIndependent Component Analysis Disentangled Representation Learning
University of Bristol
Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation
May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning Aggregation Independent Component Analysis
University of Toronto · National University of Singapore · Rutgers University
HarmonICA: Quasi-Time-Invariant ICA for Motor Unit Decomposition during Dynamic Contractions
Jun 28, 2024Alexander Kenneth Clarke, Dimitrios Halatsis, Agnese Grison +6Independent Component Analysis Surface Electromyography
Imperial College London, London, UK · Chalmers University of Technology, Gothenburg, Sweden

Nov 4, 2026J/K move · Enter open · S save

Separation Is a Riesz Energy: How Many Environments Does ICA Need?

Yuda Bi, Yubo Guo, Jiaxiao Cai, Xiaonai Li+2

Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA · Huazhong University of Science and Technology, Wuhan, China · University of Pennsylvania, Philadelphia, PA, USA+2

Independent Component Analysis

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Can independent components be recovered when all of them are Gaussian? Yes, if their variances change across environments; but how many environments, and how many samples as the number of components

d

grows? We answer both for the joint diagonalization of

K

covariances. Each component has a signature, its variances across the environments. Separating component

j

costs the Riesz 2-energy

E_j

of this signature, a sum, over the other components, of inverse pairwise Fisher informations. For Gaussian sources no estimator in

O(d)

beats

cE_j/(n+E_j)

at any

n

and

d

, and above an explicit sample size a spectral estimator attains

E_j/n

up to a constant, for every mixing and without the signatures (given a lower bound on the pairwise information). Since signatures live in

K-1

dimensions, the rate obeys a dimension law:

d^2/n

d\log d/n

and

d/n

for

K=2

3

and

K\ge4

well-spread environments. What counts is the dimension the signatures span, not their number, and

\log d

well-designed environments make

d\log d

samples suffice. Projecting the covariances onto one linear combination pays

d^2/n

at first order instead. In simulations up to

d=1000

the predictions hold and joint diagonalization with

K\ge4

attains the rate once

n/d

is in the hundreds; on task fMRI the energy says which components are hard to separate.