Abstract
The non-negative matrix factorization (NMF) algorithm is a classical matrix factorization and dimension reduction method in machine learning and data mining. However, in real problems, we always have to run the algorithm for several times and use the best matrix factorization result as the final output because of the random initialization of the matrix factorization. In this paper, we proposed an improved non-negative matrix factorization algorithm based on genetic algorithm (GA), which uses the internal parallelism and the random search of genetic algorithm to get the optimal solution of matrix factorization. It could have larger searching area and higher accuracy in matrix factorization. In the document clustering problem, we use the TDT2 dataset and design several contrast experiments on the classical NMF and the improved NMF based on genetic algorithm, the experiment results show that our improved non-negative matrix factorization algorithm has higher clustering accuracy and better robustness.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
