ArticleJournal of machine learning research : JMLR2013
Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty.
Article in Journal of machine learning research : JMLR, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed.
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Dietary patterns associated with the new onset of chronic kidney disease using clustering algorithm.Scientific reports · 2025Article
- Individualized Time-Varying Nonparametric Model With an Application in Mobile Health.Statistics in medicine · 2025Article
- Fast Fusion Clustering via Double Random Projection.Entropy (Basel, Switzerland) · 2024Article
- Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.Frontiers in molecular biosciences · 2024Review
- Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.Biometrics · 2023Article
- Nonparametric prediction distribution from resolution-wise regression with heterogeneous data.Journal of business & economic statistics : a publication of the American Statistical Association · 2023Article
- Use of Machine Learning in Stroke Rehabilitation: A Narrative Review.Brain & NeuroRehabilitation · 2022Review
- MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY.The annals of applied statistics · 2021Article
- A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion.Statistica Sinica · 2020Article
- Small area mean estimation after effect clustering.Journal of applied statistics · 2020Article
- Provable Convex Co-clustering of Tensors.Journal of machine learning research : JMLR · 2020Article
- Clustering of Data with Missing Entries using Non-convex Fusion Penalties.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2019Article
- The evolution of online ideological communities.PloS one · 2019Article
- Characterizing classes of fibromyalgia within the continuum of central sensitization syndrome.Journal of pain research · 2018Article
- Statistical modelling of citation exchange between statistics journals.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2016Article
- A New Algorithm and Theory for Penalized Regression-based Clustering.Journal of machine learning research : JMLR · 2016Article
- Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration.Journal of machine learning research : JMLR · 2016Article
- Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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
Clustering analysis is widely used in many fields. Traditionally clustering is regarded as unsupervised learning for its lack of a class label or a quantitative response variable, which in contrast is present in supervised learning such as classification and regression. Here we formulate clustering as penalized regression with grouping pursuit. In addition to the novel use of a non-convex group penalty and its associated unique operating characteristics in the proposed clustering method, a main advantage of this formulation is its allowing borrowing some well established results in classification and regression, such as model selection criteria to select the number of clusters, a difficult problem in clustering analysis. In particular, we propose using the generalized cross-validation (GCV) based on generalized degrees of freedom (GDF) to select the number of clusters. We use a few simple numerical examples to compare our proposed method with some existing approaches, demonstrating our method's promising performance.
Indexed as
Identifiers
24358018PMC3866036What 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.