Clustering of Data With Missing Entries Using Non-Convex Fusion Penalties

The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in the presence of missing information. Unlike conven...

Ausführliche Beschreibung