scientific article; zbMATH DE number 7370572
From MaRDI portal
Publication:4998948
Minjie Wang, Genevera I. Allen
Publication date: 9 July 2021
Full work available at URL: https://arxiv.org/abs/1912.05449
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
convex optimizationfeature selectionBregman divergencessparse clusteringconvex clusteringintegrative clusteringGLM deviance
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Adaptive Lasso and Its Oracle Properties
- Selection of the number of clusters via the bootstrap method
- Statistical properties of convex clustering
- Learning Markov random walks for robust subspace clustering and estimation
- Parallel multi-block ADMM with \(o(1/k)\) convergence
- Regularized \(k\)-means clustering of high-dimensional data and its asymptotic consistency
- Sparse integrative clustering of multiple omics data sets
- Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
- The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
- Fixing and extending some recent results on the ADMM algorithm
- Network exploration via the adaptive LASSO and SCAD penalties
- On the global and linear convergence of the generalized alternating direction method of multipliers
- Consistent selection of the number of clusters via crossvalidation
- A Method for Comparing Two Hierarchical Clusterings
- Sparse Convex Clustering
- Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
- Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data
- Model-Based Gaussian and Non-Gaussian Clustering
- Sparse $k$-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional Data Clustering
- Convex Clustering via l 1 Fusion Penalization
- Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
- Recovering Trees with Convex Clustering
- Clustering the mixed panel dataset using Gower's distance and k-prototypes algorithms
- Regularized boxplot via convex clustering
- Optimal Sparse Linear Prediction for Block-missing Multi-modality Data Without Imputation
- A Framework for Feature Selection in Clustering
- Convex biclustering
- When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
- On the Global Linear Convergence of the ADMM with MultiBlock Variables
- Variable Selection for Model-Based Clustering
- The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent