arXiv research

CW-ICA improves on ANICA for non-linear source separation.

New model separates images into independent factors quickly and easily.

Robust methods for nonlinear ICA improve performance in outlier presence.

Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we first propose a new intuitive principle of unsupervised deep learning from time series which uses the nonstationary structure of the data. Our l…

A new ICA method adds L1-regularization for better interpretability of fMRI data.

OT-ICA uses optimal transport to find independent components, outperforming traditional methods.

Independent component analysis (ICA) is the most popular method for blind source separation (BSS) with a diverse set of applications, such as biomedical signal processing, video and image analysis, and communications. Maximum likelihood (ML), an optimal theoretical framework for ICA, requires knowledge of the true unde…

Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals. We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gau…

Paper proposes compressive ICA algorithms for ICA model.

ICA accurately estimates treatment effects even with confounders.

New ICA method improves on existing techniques.

Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent. Though ICA has proven useful and has been employed in many applications, complete statistical independence can be too restrictive an assumption in practice. Additiona…

New model combines ICA and HMM for unsupervised learning of nonstationary time series.

WICA improves ICA results with a new method.

AVICA estimates noise levels for better group ICA source recovery.

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…

Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of fourth-order mom…

This paper reviews nonlinear ICA for disentangled representations in unsupervised learning.

Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., identifiability). Recently, the very first identifiability proofs for nonlinear ICA have been proposed, leveraging the temporal structure of the…

Generalizes PCA and ICA for continuous-time signals using neural networks.

New ICA method for sources with mixed spectra.

New framework for identifying spatial data components using TP latent components.

A new method improves ICA performance by approximating MDI.

New methods generalize nonlinear ICA beyond structural sparsity.

Causal Mosaic distinguishes cause from effect using nonlinear ICA and ensemble methods.

Paper analyzes online tensorial ICA convergence with stochastic approximation.

We performed an empirical comparison of ICA and PCA algorithms by applying them on two simulated noisy time series with varying distribution parameters and level of noise. In general, ICA shows better results than PCA because it takes into account higher moments of data distribution. On the other hand, PCA remains quit…

New method identifies latent sources from nonlinear mixtures without auxiliary variables.

Independent component analysis (ICA) is a widely used BSS method that can uniquely achieve source recovery, subject to only scaling and permutation ambiguities, through the assumption of statistical independence on the part of the latent sources. Independent vector analysis (IVA) extends the applicability of ICA by joi…

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…

New theory allows ICA without assuming non-Gaussian sources.

This paper is an attempt to separate cardiac and respiratory signals from an electrical bio-impedance (EBI) dataset. For this two well-known algorithms, namely Principal Component Analysis (PCA) and Independent Component Analysis (ICA), were used to accomplish the task. The ability of the PCA and the ICA methods first …

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…

Reservoir subspace injection improves online ICA by preserving injected features.

Paper connects contrastive learning to MI maximization and establishes robust methods for nonlinear ICA and subspace estimation.

Neural network estimates mutual information for ICA.

New model improves transfer learning and semi-supervised learning.

Independent Component Analysis (ICA) is the problem of learning a square matrix $A$, given samples of $X=AS$, where $S$ is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each $S_i$ has finite and moderately valued fourth moment. However, there are practical appli…

We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techni…

Contrastive ICA identifies features in experimental groups relative to controls.

Half-AVAE enhances VAE for underdetermined ICA with adversarial training.

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…

New linear algorithms improve ICA over finite fields with lower bounds.

Study uses Wasserstein distance to identify causal orders and unmix sources.

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We i…

Theoretical validation of linear PCA and ICA for accurate nonlinear BSS.

We develop a mean-field theory for multi-component ICA in high dimensions.

FCA unmixes matrices from mixtures using free probability theory.