Conference proceeding
Convex clustering and recovery of partially observed data
2016 IEEE International Conference on Image Processing (ICIP), Vol.2016-, pp.3498-3502
09/2016
DOI: 10.1109/ICIP.2016.7533010
PMID: 33619429
Abstract
We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.
Details
- Title: Subtitle
- Convex clustering and recovery of partially observed data
- Creators
- Sunrita Poddar - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USAMathews Jacob - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2016 IEEE International Conference on Image Processing (ICIP), Vol.2016-, pp.3498-3502
- DOI
- 10.1109/ICIP.2016.7533010
- PMID
- 33619429
- NLM abbreviation
- Proc Int Conf Image Proc
- ISSN
- 1522-4880
- eISSN
- 2381-8549
- Publisher
- IEEE
- Language
- English
- Date published
- 09/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070707402771
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