An MM Estimation Algorithm for Independent Component Analysis.

Xun-Jian Li, Hua Zhou, Kenneth Lange

Abstract read

In one paragraph

Article in Signal processing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xun-Jian LiDepartment of Biostatistics, University of California, Los Angeles, CA.

Hua ZhouDepartments of Biostatistics and Computational Medicine, University of California, Los Angeles, CA.

Kenneth LangeDepartments of Computational Medicine, Human Genetics, and Statistics, University of California, Los Angeles, CA.

Funding

Genomics, GPUs, and Next Generation Computational StatisticsR01HG006139 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SOBEL, ERIC · 2011 to 2023

$5.1M

Modeling, Inference, and Optimization for Genomic and Biomedical Big DataR35GM141798 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LANGE, KENNETH L · 2021 to 2025

$2.7M

Transforming Precision Medicine: Dynamic Learning and Prediction of Disease Progression in Massive, Diverse, and Multimodal CohortsR01DK142026 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GANG LI, Hua Zhou · 2025 to 2026

$1.1M

NHGRI NIH HHS R01 HG006139NIDDK NIH HHS R01 DK142026NIGMS NIH HHS R35 GM141798

6 · The paper itself

Abstract

Estimating the unmixing matrix in independent component analysis (ICA) by maximizing the statistical independence of the sources has been a central problem over the past few decades. This paper exploits the minorization-maximization (MM) principle by constructing a quadratic lower-bound surrogate for its log-likelihood. We derive an explicit update that satisfies the surrogate's first-order stationarity condition using the Fan-von Neumann inequality. Its cost per iteration is dominated by two matrix-matrix multiplications and a singular value decomposition of a small square matrix, resulting in low computational overhead and making the method amenable to GPU acceleration. Across three real datasets, the proposed MM method consistently runs faster than three Newton-type algorithms, including Picard's method, while attaining comparable or higher loglikelihood values. Particularly, the GPU version of MM is faster by an order of magnitude.

Indexed as

Fan–von Neumann inequalityICAloglikelihoodMMquadratic lower bound principle

Identifiers

PMID42852254

DOI10.1016/j.sigpro.2026.110633

PMCPMC13647453

What OpenQuestion holds

Textmetadata

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.