Boosting improves ICA for better component recovery.
problem Improving ICA's reliance on prior knowledge of sources.
method Maximizing likelihood via boosting and fixed-point unmixing.
result Boosting-based ICA outperforms existing methods.
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48 results for “Independent-Component”
New ICA method improves on existing techniques.
problem Finding independent components in data.
method Multiple-weighted Independent Component Analysis (MWeICA) based on approximate diagonalization of weighted covariance matrices.
result MWeICA achieves better results than state-of-the-art ICA methods with similar computational time.
New algorithm improves source separation with multi-trial supervision.
problem Non-convex optimization and interpretability of independent components.
method Proximal gradient-type algorithm in invertible matrices with backpropagation for joint learning.
result Increased success rate of non-convex optimization and improved interpretability.
Subjective classification of galaxies can mislead us in the quest of the origin regarding formation and evolution of galaxies since this is necessarily limited to a few features. The human mind is not able to apprehend the complex correlations in a manyfold parameter space, and multivariate analyses are the best tools …
OT-ICA uses optimal transport to find independent components, outperforming traditional methods.
problem Finding independent components from linear mixtures of signals.
method OT-ICA uses the squared Wasserstein distance to maximize non-Gaussianity, optimizing projections via gradient descent.
result OT-ICA outperforms traditional proxy-based methods in various applications.
Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other. Usually the ICA framework assumes a model according to which the observations are generated (such as a linear tra…
Second-order Approximation of Minimum Discrimination Information in Independent Component Analysisstat.ML
A new method improves ICA performance by approximating MDI.
problem Improving F astICA's performance with nonlinear functions.
method Second-order approximation of MDI for joint maximization.
result Efficiency validated through experiments compared to other ICA algorithms.
New algorithm tackles hidden confounders in causal discovery.
problem Hidden confounders make causal discovery difficult.
method LFOICA estimates mixing matrix directly without parametric assumptions.
result Computational efficiency makes causal discovery more feasible.
Generative model identifies temporal count data components with regime-dependent contributions.
problem Modeling temporal count data with regime-dependent dynamics.
method Generative framework combining regime-adaptive dynamics with Poisson log-normal emissions.
result Established identifiability of the model and revealed co-variation patterns and regime shifts.
Contrastive ICA identifies features in experimental groups relative to controls.
problem Jointly analyzing experimental and control datasets to identify salient features.
method Developed contrastive ICA (cICA) using tensor decomposition.
result cICA identifies patterns and visualizes data effectively, outperforming existing methods.
We apply both distance-based (Jin and Matteson, 2017) and kernel-based (Pfister et al., 2016) mutual dependence measures to independent component analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel m…
DDICA separates nonlinear mixed signals robustly.
problem Blind source separation of nonlinear mixed signals.
method Deep deterministic neural network with matrix-based entropy function.
result DDICA effectively separates independent components with high accuracy.
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations …
Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles. The goal of this tutorial is to prov…
New framework extends ICA for non-independent variables, identifying pairwise mean independence.
problem Non-independent variables complicating ICA recovery.
method Algebraic recovery algorithm based on least-squares optimization over the orthogonal group.
result Pairwise mean independence is identifiable, robust to independence constraints.
ICA accurately estimates treatment effects even with confounders.
problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networkscs.LG
A new IC layer combines BN and Dropout for faster neural network training.
problem Training inefficiency in deep neural networks.
method Introducing an IC layer before weight layers, combining BN and Dropout.
result Consistently outperforms baseline approaches with faster convergence.
Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have…
This paper reviews nonlinear ICA for disentangled representations in unsupervised learning.
problem Finding useful disentangled representations in unsupervised deep learning.
method Review of nonlinear ICA theory and algorithms for disentanglement.
result Nonlinear ICA can be shown to estimate useful disentangled representations.
New method recovers latent sources from multiple noisy views using deep neural networks.
problem Recovering a common latent source from multiple nonlinearly mixed views.
method Novel identifiability proofs using deep neural networks.
result Independent latent sources can be recovered from multiple noisy views using deep neural networks.
New method learns disentangled signals without prior or model constraints.
problem Learning disentangled signals from data without prior or model constraints.
method Minimizes conditional KL divergence using a sequential algorithm to learn de-mixing flow models.
result Method learns self-sufficient signals that can reconstruct missing values.
A new method for binary ICA using non-stationary sources.
problem Independent component analysis of binary data.
method Linear mixing model in latent space, followed by binary observation model with non-stationary sources.
result Proves non-identifiability with few observed variables but identifies with more variables.
Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model identifiability. Linear non-Gaussian component analysis (LNGCA) generalizes the ICA model t…
Half-AVAE enhances VAE for underdetermined ICA with adversarial training.
problem Challenges in ICA under underdetermined conditions.
method Encoder-free VAE with adversarial networks and EE terms.
result Half-AVAE outperforms baseline models in underdetermined ICA.
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian independent sources. The maximization of the corresponding likelihood is a challen…
WICA improves ICA results with a new method.
problem Finding independent components in nonlinear data.
method New nonlinear ICA model (WICA) with efficient correlation coefficient verification.
result WICA yields better and more stable results than other algorithms.
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding. It extends the ordinary ICA model in a theoretical…
Recently, there has been a trend to combine independent component analysis and canonical polyadic decomposition (ICA-CPD) for an enhanced robustness for the computation of CPD, and ICA-CPD could be further converted into CPD of a 5th-order partially symmetric tensor, by calculating the eigenmatrices of the 4th-order cu…
New method for separating mixed signals with nonlinear functions.
problem Recovering source signals from nonlinear mixtures.
method Optimisation-based function approximation to minimize mutual statistical dependence.
result The method can recover source signals from nonlinear mixtures under certain conditions.
Sparse Gaussian ICAstat.ML
Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS where A is an unknown mixing matrix. Critically, all existing methods for ICA rely on and exploit strongly the assumption that S is not Gaussian…
Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the independent components one after another. A novel method for deflationary ICA, referr…
Identifies causal effects in LiNGAM models with latent variables.
problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.
We introduce three novel semi-parametric extensions of probabilistic canonical correlation analysis with identifiability guarantees. We consider moment matching techniques for estimation in these models. For that, by drawing explicit links between the new models and a discrete version of independent component analysis …
Paper analyzes online tensorial ICA convergence with stochastic approximation.
problem Online tensorial ICA convergence analysis.
method Stochastic approximation for nonconvex optimization.
result Sharp finite-sample error bound of .
Study improves probabilistic circuits using transformations for better predictions.
problem Predictive limitations of probabilistic circuits in robotic scenarios.
method Integrates transformations into joint probability trees, extending their capabilities.
result Achieves higher likelihoods with fewer parameters on various data sets.
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are cap…
Faster, context-aware topic modeling approach unveiled.
problem Slow and heavy preprocessing in topic modeling.
method Conceptualizes topics as independent axes, decomposes embeddings using ICA.
result 4.5x faster than BERTopic on average, with diverse and coherent topics.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
problem Identifying pollution sources in rivers for effective environmental management.
method Principal Component Analysis and its modifications, along with Independent Component Analysis and Factor Analysis, are applied to nitrogen pollution source identification.
result PCA and related techniques can be powerful tools for uncovering nitrogen pollution sources in rivers.
Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Cl…
New approach tackles nonidentifiability in nonlinear blind source separation.
problem Nonidentifiability in nonlinear blind source separation.
method Independent mechanism analysis, incorporating causal assumptions.
result Empirical and theoretical evidence shows improved identifiability.
Paper proposes a robust method for federated ICA with geometric median aggregation.
problem Federated ICA with permutation ambiguity in client estimations.
method Geometric median aggregation with k-means clustering to resolve permutation ambiguity.
result The method provably remains effective in highly heterogeneous scenarios.
Study on robustness of unsupervised representation learning in slightly misspecified settings.
problem Identify nonlinear representation learning in slightly misspecified settings.
method Formalize and investigate Independent Component Analysis (ICA) with observations generated by a mixing function close to a local isometry.
result Approximate identifiability of nonlinear ICA with almost isometric mixing functions.
Reservoir subspace injection improves online ICA by preserving injected features.
problem Discarding injected features in top- whitening can degrade performance.
method Formalized reservoir subspace injection (RSI) and developed diagnostics (IER, SSO, ρ_x) to identify and mitigate the failure mode.
result RSI controller preserves passthrough retention, improving performance by up to 2.2 dB.
Independent component analysis (ICA) is a widespread data exploration technique, where observed signals are modeled as linear mixtures of independent components. From a machine learning point of view, it amounts to a matrix factorization problem with a statistical independence criterion. Infomax is one of the most used…
ICLabel: An automated electroencephalographic independent component classifier, dataset, and websiteeess.SP
ICLabel automates EEG IC classification, improving accuracy and speed.
problem Manual IC classification is time-consuming and requires expertise.
method Automated classifier using crowdsourced labels and improved efficiency.
result ICLabel classifier outperforms existing methods in accuracy and speed.
Compressive learning framework adapted for semi-parametric models.
problem Handling large datasets efficiently with semi-parametric models.
method Reformulate compressive learning framework to handle semi-parametric models, capturing their inherent topology and structure.
result Demonstrated robustness and efficiency of the framework in independent component analysis and subspace clustering.
New framework for disentangling features from noisy data.
problem Disentangling identifiable features from noisy data.
method Structured Nonlinear Independent Component Analysis (SNICA).
result Identifiability holds even in the presence of noise of unknown distribution.
Financial markets modeled like brain networks using dMNC.
problem Understanding latent dynamics in financial markets.
method Biologically inspired framework using dMNC.
result Structural persistence, regime shifts, and early warning signals identified.