An Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning

Authors(4) :-F. Femila, G. Sridevi, D. Swathi, K. Swetha

Missing data padding is an important problem that is faced in real time. This makes the task of data processing challenging. This paper aims to design a solution for this problem which is ways different from traditional approaches. The proposed method is based on co-cluster sparse matrix learning (CCSML) method. This algorithm learns without reference class, and even with data continuous missing rate as high as the existing techniques. This method is based on a tensor optimization model and labeled maximum block. The computational models of sparse recovery learning are based on low-rank matrix and co-clusters of genome-wide association study (GWAS) data matrices, and the performance is better than existing techniques.

Authors and Affiliations

F. Femila
Department of Computer Science, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
G. Sridevi
Department of Computer Science, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
D. Swathi
Department of Computer Science, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
K. Swetha
Department of Computer Science, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Data Preprocessing, Missing Value, Co-Cluster Sparse Matrix, Sparse Recovery

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 215-222
Manuscript Number : CSEIT195220
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

F. Femila, G. Sridevi, D. Swathi, K. Swetha, "An Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.215-222, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195220
Journal URL : http://ijsrcseit.com/CSEIT195220

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