Conference proceeding
Clustering of Data with Missing Entries
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2018-, pp.2831-2835
04/2018
DOI: 10.1109/ICASSP.2018.8462602
PMID: 33633499
Abstract
The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering algorithm, that will provide good clustering even in the presence of missing data. The proposed technique solves an l o fusion penalty based optimization problem to recover the clusters. We theoretically analyze the conditions needed for the successful recovery of the clusters. We also propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. The method is demonstrated on simulated and real datasets, and is observed to perform well in the presence of large fractions of missing entries.
Details
- Title: Subtitle
- Clustering of Data with Missing Entries
- Creators
- Sunrita Poddar - Department of Electrical and Computer Engineering, University of Iowa, IA, USAMathews Jacob - Department of Electrical and Computer Engineering, University of Iowa, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2018-, pp.2831-2835
- DOI
- 10.1109/ICASSP.2018.8462602
- PMID
- 33633499
- NLM abbreviation
- Proc IEEE Int Conf Acoust Speech Signal Process
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070423702771
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