ArticleProceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management2018
COPA: Constrained PARAFAC2 for Sparse & Large Datasets.
Article in Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Unraveling Complex Temporal Patterns in EHRs via Robust Irregular Tensor Factorization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning for Computational Phenotyping.Proceedings of machine learning research · 2023Article
- Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data.Neuroinformatics · 2023Article
- Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches.Frontiers in neuroscience · 2022Article
- LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2020Article
- TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Distributed Tensor Decomposition for Large Scale Health Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. Despite recent improvements on unconstrained PARAFAC2, its model factors are usually dense and sensitive to noise which limits their interpretability. As a result, the following open challenges remain: a) various modeling constraints, such as temporal smoothness, sparsity and non-negativity, are needed to be imposed for interpretable temporal modeling and b) a scalable approach is required to support those constraints efficiently for large datasets. To tackle these challenges, we propose a
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Registered trials
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.