arXiv Machine Learning June 5, 2026 By Yue Zhao, Thierry Chekouo, Sandra Safo

Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

Read the original on arXiv Machine Learning →

arXiv:2606. 05488v1 Announce Type: cross Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data.

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arXiv Machine Learning

Aug 18

Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)

arXiv:2608. 16029v1 Announce Type: new Abstract: Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks.

By Oktay Agcaoglu

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arXiv Machine Learning

Jun 3

A Fast Screening Approach for High-dimensional Outcomes and High-dimensional Predictors

arXiv:2606. 03018v1 Announce Type: cross Abstract: Modeling interactions among multimodal, high-dimensional data is intrinsically challenging due to ultra-high dimensionality and complex dependence structure with high level noise.

By Hongju Park, Zhenyao Ye, Shuo Chen

multimodal safety

arXiv Machine Learning

Aug 26

A Structural FHMM for Interpretable Disease Trajectories in T2DM

arXiv:2608.24328v1 Announce Type: new Abstract: In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Typ...

By Alessandro Mari, Ekaterina Krymova, Guillaume Obozinski, Maria Luisa Marques de Sa Faquetti, Adrian Martinez de la Torre, Andrea Burden

arXiv AI

Jun 10

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

By Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass

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arXiv Machine Learning

Jul 27

Longitudinal Random Forests for Sparse and Irregular Response Trajectories

arXiv:2607. 21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points.

By Yangsheng Wang, Xiaotian Dai, Haoda Fu, Guifang Fu

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arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

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Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)

A Fast Screening Approach for High-dimensional Outcomes and High-dimensional Predictors

A Structural FHMM for Interpretable Disease Trajectories in T2DM

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PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data