Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

249
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
249
Survival Tree01:19

Survival Tree

358
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
358
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
Correlation and Regression00:53

Correlation and Regression

3.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Gut microbiota transfer from autoimmune dry eye mice imprints stereotypic B cell receptor repertoires in the lacrimal gland and induces disease.

Frontiers in immunology·2026
Same author

Low-Temperature Deposition of Polycrystalline ε-Ga<sub>2</sub>O<sub>3</sub> for Deep Ultraviolet Perceptual Photodetection.

The journal of physical chemistry letters·2026
Same author

Genomic analysis of <i>Botrytis cinerea</i> causing postharvest strawberry rot and the control effect of pydiflumetofen.

Frontiers in microbiology·2026
Same author

Spatial multi-omics reveals region-specific molecular signatures in a 6-OHDA model of Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Nonlinear Modeling Reveals Novel Associations Between Genetically Predicted Protein Levels and Pancreatic Cancer Risk.

Molecular carcinogenesis·2026
Same author

A large-scale vision foundation model for musculoskeletal radiographs.

NPJ digital medicine·2026
Same journal

Classification Under Local Differential Privacy with Model Reversal and Model Averaging.

Journal of machine learning research : JMLR·2026
Same journal

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same journal

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same journal

Unsupervised Tree Boosting for Learning Probability Distributions.

Journal of machine learning research : JMLR·2026
Same journal

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.

Journal of machine learning research : JMLR·2026
Same journal

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes.

Journal of machine learning research : JMLR·2026
See all related articles

Related Experiment Video

Updated: Jan 4, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

A New Algorithm and Theory for Penalized Regression-based Clustering.

Chong Wu1, Sunghoon Kwon2, Xiaotong Shen3

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.

Journal of Machine Learning Research : JMLR
|October 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces DC-ADMM, a faster penalized regression clustering algorithm. It offers improved computational efficiency and theoretical guarantees for clustering consistency, making it scalable for large datasets.

Keywords:
Alternating direction method of multipliers (ADMM)Clustering consistencyDifference of convex (DC) programmingTruncated L1-penalty (TLP)

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Related Experiment Videos

Last Updated: Jan 4, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Area of Science:

  • Statistics
  • Machine Learning
  • Computational Statistics

Background:

  • Clustering is an unsupervised learning technique.
  • Penalized regression with grouping pursuit offers a method for clustering.
  • Existing algorithms may lack computational efficiency and theoretical guarantees.

Purpose of the Study:

  • To develop a more efficient and scalable algorithm for penalized regression clustering.
  • To establish a new theory of clustering consistency for this method.
  • To provide an R package for practical implementation.

Main Methods:

  • The study proposes the DC-ADMM algorithm, combining difference of convex (DC) programming with the alternating direction method of multipliers (ADMM).
  • Theoretical analysis includes establishing a finite-sample mis-clustering error bound for L0 constrained regularization.
  • Numerical comparisons were made against a quadratic penalty-based algorithm.

Main Results:

  • The DC-ADMM algorithm demonstrates superior computational efficiency due to closed-form updating formulas.
  • Numerical experiments confirm the utility and scalability of DC-ADMM.
  • Theoretical results provide conditions for clustering consistency.

Conclusions:

  • DC-ADMM is an efficient and scalable algorithm for penalized regression clustering.
  • The theoretical framework supports clustering consistency guarantees.
  • The R package 'prclust' facilitates the application of these methods.