ArticleJournal of machine learning research : JMLR2016
A New Algorithm and Theory for Penalized Regression-based Clustering.
Article in Journal of machine learning research : JMLR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed.
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.Biometrics · 2023Article
- Biclustering analysis of functionals via penalized fusion.Journal of multivariate analysis · 2022Article
- Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.Journal of machine learning research : JMLR · 2021Article
- A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion.Statistica Sinica · 2020Article
- Provable Convex Co-clustering of Tensors.Journal of machine learning research : JMLR · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit, as demonstrated in Pan et al. (2013). In this paper, we develop a more efficient algorithm for scalable computation and a new theory of clustering consistency for the method. This algorithm, called DC-ADMM, combines difference of convex (DC) programming with the alternating direction method of multipliers (ADMM). This algorithm is shown to be more computationally efficient than the quadratic penalty based algorithm of Pan et al. (2013) because of the former's closed-form updating formulas. Numerically, we compare the DC-ADMM algorithm with the quadratic penalty algorithm to demonstrate its utility and scalability. Theoretically, we establish a finite-sample mis-clustering error bound for penalized regression based clustering with the
Indexed as
Identifiers
31662706PMC6818515What 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.