ArticleJournal of machine learning research : JMLR2018
An
Article in Journal of machine learning research : JMLR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- An analysis of classical multidimensional scaling with applications to clustering.Information and inference : a journal of the IMA · 2023Article
- D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.Journal of machine learning research : JMLR · 2022Article
- Asymptotic Theory of Eigenvectors for Random Matrices with Diverging Spikes.Journal of the American Statistical Association · 2022Article
- BRIDGING CONVEX AND NONCONVEX OPTIMIZATION IN ROBUST PCA: NOISE, OUTLIERS, AND MISSING DATA.Annals of statistics · 2021Article
- Robust high dimensional factor models with applications to statistical machine learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- ASYMMETRY HELPS: EIGENVALUE AND EIGENVECTOR ANALYSES OF ASYMMETRICALLY PERTURBED LOW-RANK MATRICES.Annals of statistics · 2021Article
- ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK.Annals of statistics · 2020Article
- SPECTRAL METHOD AND REGULARIZED MLE ARE BOTH OPTIMAL FOR TOP-Annals of statistics · 2019Article
- LARGE COVARIANCE ESTIMATION THROUGH ELLIPTICAL FACTOR MODELS.Annals of statistics · 2018Article
- Robust High-dimensional Volatility Matrix Estimation for High-Frequency Factor Model.Journal of the American Statistical Association · 2018Article
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
In statistics and machine learning, we are interested in the eigenvectors (or singular vectors) of certain matrices (e.g. covariance matrices, data matrices, etc). However, those matrices are usually perturbed by noises or statistical errors, either from random sampling or structural patterns. The Davis-Kahan sin
Indexed as
Identifiers
31749664PMC6867801What 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.