2017/09/06 by Sunrita Poddar, Poddar, Sunrita, Mathews Jacob +1 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Clustering Algorithms Research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Traffic Prediction and Management Techniques #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1709.01870
arxiv created 2017/09/06 · openalex publication_date 2017/09/06 · arxiv updated 2017/09/07 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
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 conventional clustering techniques where every feature is known for each point, our algorithm can handle cases where a few feature values are unknown for every point. For this more challenging problem, we provide theoretical guarantees for clustering using a ℓ0 fusion penalty based optimization problem. Furthermore, we propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. It is observed that this algorithm produces solutions that degrade gradually with an increase in the fraction of missing feature values. We demonstrate the utility of the proposed method using a simulated dataset, the Wine dataset and also an under-sampled cardiac MRI dataset. It is shown that the proposed method is a promising clustering technique for datasets with large fractions of missing entries.