An Efficient Semismooth Newton Based Algorithm for Convex Clustering

Keyphrases

• Semismooth Newton 100%
• Convex Clustering 100%
• Large-scale Problems 66%
• Clustering Model 66%
• Numerical Results 33%
• Numerical Experiments 33%
• Local Minima 33%
• Numerical Algorithms 33%
• Augmented Lagrangian Method 33%
• K-means 33%
• Superior Performance 33%
• First-order Methods 33%
• Clustering Problem 33%
• Hierarchical Clustering 33%
• Unsupervised Learning 33%
• Convex Relaxation 33%
• Alternating Minimization 33%
• Alternating Direction multiplier Method 33%
• Langer 33%
• Sums of Norms 33%
• Popular 33%
• High Efficiency 33%

Mathematics

• Scale Problem 100%
• Lagrangian 50%
• Hierarchical Clustering 50%
• Model Chi 50%
• Numerical Algorithm 50%
• K-Means 50%
• Real Data 50%
• Numerical Experiment 50%
• Local Minimum 50%
• Simulated Data 50%
• Alternating Direction Method of Multipliers 50%

Computer Science

• Large-Scale Problem 100%
• Unsupervised Learning 50%
• Local Minimum 50%
• Superior Performance 50%
• Convex Relaxation 50%
• Alternating Direction Method of Multipliers 50%
• Fundamental Problem 50%
• Hierarchical Clustering 50%