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

Authors

  • 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

DOI:

https://doi.org//10.32628/CSEIT195220

Keywords:

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

Abstract

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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
F. Femila, G. Sridevi, D. Swathi, K. Swetha, " An Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning, IInternational 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