Journal article
Clustering of Data With Missing Entries Using Non-Convex Fusion Penalties
IEEE transactions on signal processing, Vol.67(22), pp.5865-5880
11/15/2019
DOI: 10.1109/TSP.2019.2944758
PMCID: PMC7929088
PMID: 33664558
Abstract
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 l_0 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 the ASL dataset. It is shown that the proposed method is a promising clustering technique for datasets with large fractions of missing entries.
Details
- Title: Subtitle
- Clustering of Data With Missing Entries Using Non-Convex Fusion Penalties
- Creators
- Sunrita Poddar - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USAMathews Jacob - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on signal processing, Vol.67(22), pp.5865-5880
- DOI
- 10.1109/TSP.2019.2944758
- PMID
- 33664558
- PMCID
- PMC7929088
- NLM abbreviation
- IEEE Trans Signal Process
- ISSN
- 1053-587X
- eISSN
- 1941-0476
- Publisher
- IEEE
- Grant note
- 1R01EB01996101A1 / Grant
- Language
- English
- Date published
- 11/15/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070648802771
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