ArticleJournal of machine learning research : JMLR2020
Provable Convex Co-clustering of Tensors.
Article in Journal of machine learning research : JMLR, 2020. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Optimal variable clustering for high-dimensional matrix valued data.Information and inference : a journal of the IMA · 2025Article
- Estimating Higher-Order Mixed Memberships via theJournal of the American Statistical Association · 2025Article
- Multi-way overlapping clustering by Bayesian tensor decomposition.Statistics and its interface · 2024Article
- Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.Journal of machine learning research : JMLR · 2021Article
- Multiway Graph Signal Processing on Tensors: Integrative analysis of irregular geometries.IEEE signal processing magazine · 2020Article
- Generalized Co-Clustering Analysis via Regularized Alternating Least Squares.Computational statistics & data analysis · 2020Article
- Discovering Geometry in Data Arrays.Computing in science & engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising "blessing of dimensionality" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.
Indexed as
Identifiers
33312074PMC7731944What 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.