Open AccessAn Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning
Author(s) -
F. Femila,
G. Sridevi,
D. Swathi,
K.H. Swetha
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195220
Subject(s) - computer science , sparse matrix , imputation (statistics) , missing data , padding , data mining , matrix (chemical analysis) , artificial intelligence , machine learning , algorithm , physics , materials science , computer security , quantum mechanics , composite material , gaussian
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.