Matrix completion algorithms with applications in biomedicine, e-commerce and social science

This thesis investigates matrix completion algorithms with applications in biomedicine, e-commerce and social science. In general, matrix completion algorithms work well for low rank matrices. Such matrices find many applications in recommender systems and social network analysis. On the other hand, biological networks often yield high rank matrices. For example, the adjacency matrix representing interactions between transcription factors and target genes in the cell is a highly sparse matrix, in which most entries correspond to absent interactions and only a few entries correspond to present interactions. This sparse matrix is a high rank or even full rank matrix. Matrix completion algorithms do not work well for high rank matrices. In this thesis, several experiments are conducted to evaluate the performance of matrix completion algorithms for both low rank and high rank matrices. A new high rank matrix completion method is proposed, which is designed to process adjacency matrices representing interactions between transcription factors and target genes in cells.

Name thesis:degree_name

Master of Science in Computer Science - (M.S.)

Discipline thesis:degree_discipline

Computer Science

Year

2017

Author dc:creator

• Wang, Yiran

Contributors dc:contributor

• Jason T. L. Wang
• Xiaoning Ding
• Chase Qishi Wu

• Matrix completion algorithms
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/37

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1036