Data Matrix Completion Based on Pattern Classification
Authors: 
Siyuan Lu, Xiaolan Tang, Yu Liu, Wenlong ChenAuthors Info & Claims
Siyuan Lu, Xiaolan Tang, Yu Liu, Wenlong ChenAuthors Info & ClaimsArticle No.: 16, Pages 1 - 5
Abstract
In recent years, with the rapid development of big data technology, the matrix completion is often used for data recovery, and how to improve the accuracy of matrix completion is a key issue. This paper proposes a matrix completion method based on pattern classification, called PCRE, to improve data recovery performance. Since the hidden similarity within the data is a significant factor affecting the overall performance, the method PCRE uses non-negative matrix decomposition to extract the patterns of the data and accordingly rearranges the data matrix to fit for the matrix completion. Experiments are conducted by using PM 10 monitoring data collected by 34 sensors in Beijing in 2019 (totally 351 days). The results show that, compared with existing methods, PCRE improves the accuracy of data recovery with a shorter computation time.
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Published: 07 December 2021
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- the Science & Technology Project of Beijing Municipal Commission of Education in China
- the Beijing Natural Science Foundation
- the National Natural Science Foundation of China
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CSAE 2021
CSAE 2021: The 5th International Conference on Computer Science and Application Engineering
October 19 - 21, 2021
Sanya, China
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Overall Acceptance Rate 368 of 770 submissions, 48%
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