ArticleJournal of the American Statistical Association2020
D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.Electronic journal of statistics · 2025Article
- Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis.Journal of the American Statistical Association · 2023Article
- Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data.Frontiers in genetics · 2023Article
- Generalized Liquid Association Analysis for Multimodal Data Integration.Journal of the American Statistical Association · 2023Article
- Article
- Interpretive JIVE: Connections with CCA and an application to brain connectivity.Frontiers in neuroscience · 2022Article
- CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.Electronic journal of statistics · 2022Article
- D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.Journal of machine learning research : JMLR · 2022Article
- Integrative Factor Regression and Its Inference for Multimodal Data Analysis.Journal of the American Statistical Association · 2022Article
- Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.IEEE transactions on medical imaging · 2021Article
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing decomposition methods often focus on the orthogonality between the common and distinctive matrices, but inadequately consider the more necessary orthogonal relationship between the two distinctive matrices. The latter guarantees that no more shared information is extractable from the distinctive matrices. We propose decomposition-based canonical correlation analysis (D-CCA), a novel decomposition method that defines the common and distinctive matrices from the
Indexed as
Identifiers
What OpenQuestion holds
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.