ArticleComputer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision2014
Canonical Correlation Analysis on Riemannian Manifolds and Its Applications.
Article in Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.Electronic journal of statistics · 2025Article
- Slow and steady: auditory features for discriminating animal vocalizations.bioRxiv : the preprint server for biology · 2024Article
- Assessment of cognitive biases in Augmented Reality: Beyond eye tracking.Journal of eye movement research · 2022Article
- An Online Riemannian PCA for Stochastic Canonical Correlation Analysis.Advances in neural information processing systems · 2021Article
- Gaze training supports self-organization of movement coordination in children with developmental coordination disorder.Scientific reports · 2019Article
- Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.Brain imaging and behavior · 2019Article
- A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
- Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-valued Data.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2017Article
- Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2017Article
- Conditional local distance correlation for manifold-valued data.Information processing in medical imaging : proceedings of the ... conference · 2017Article
- Nonlinear regression on Riemannian manifolds and its applications to Neuro-image analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2015Article
- Manifold-valued Dirichlet Processes.JMLR workshop and conference proceedings · 2015Article
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
Canonical correlation analysis (CCA) is a widely used statistical technique to capture correlations between two sets of multi-variate random variables and has found a multitude of applications in computer vision, medical imaging and machine learning. The classical formulation assumes that the data live in a pair of
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.