Abstract:
Matrix completion is the process of estimating missing entries from a matrix using some prior knowledge. Typically, the prior knowledge is that the matrix is low-rank. In...Show MoreMetadata
Abstract:
Matrix completion is the process of estimating missing entries from a matrix using some prior knowledge. Typically, the prior knowledge is that the matrix is low-rank. In this paper, we present an extension of standard matrix completion that leverages prior knowledge that the matrix is low-rank and that the data samples can be efficiently represented by a fixed known dictionary. Specifically, we compute a low-rank representation of a data matrix with respect to a given dictionary using only a few observed entries. A novel modified version of the singular value thresholding (SVT) algorithm named joint low-rank representation and matrix completion SVT (J-SVT) is proposed. Experiments on simulated data show that the proposed J-SVT algorithm provides better reconstruction results compared to standard matrix completion.
Date of Conference: 01-05 September 2014
Date Added to IEEE Xplore: 13 November 2014
Electronic ISBN:978-0-9928-6261-9
ISSN Information:
Conference Location: Lisbon, Portugal
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- IEEE Keywords
- Index Terms
- Singular Value ,
- Matrix Completion ,
- Low-rank Representation ,
- Singular Value Thresholding ,
- Joint Matrix ,
- Data Matrix ,
- Matrix Representation ,
- Missing Entries ,
- Optimization Problem ,
- Experimental Evaluation ,
- Positive Constant ,
- Ratio Of Samples ,
- Processing Algorithms ,
- Singular Value Decomposition ,
- Sparse Matrix ,
- Convex Optimization Problem ,
- Original Matrix ,
- Rank Of Matrix ,
- Nuclear Norm ,
- Original Data Matrix ,
- Nuclear Norm Minimization ,
- Dictionary Size
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Singular Value ,
- Matrix Completion ,
- Low-rank Representation ,
- Singular Value Thresholding ,
- Joint Matrix ,
- Data Matrix ,
- Matrix Representation ,
- Missing Entries ,
- Optimization Problem ,
- Experimental Evaluation ,
- Positive Constant ,
- Ratio Of Samples ,
- Processing Algorithms ,
- Singular Value Decomposition ,
- Sparse Matrix ,
- Convex Optimization Problem ,
- Original Matrix ,
- Rank Of Matrix ,
- Nuclear Norm ,
- Original Data Matrix ,
- Nuclear Norm Minimization ,
- Dictionary Size
- Author Keywords