id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_sais73suafgozanfdqttn5js4i	Shiping Wang	Penalized nonnegative matrix tri-factorization for co-clustering	2017	6	.pdf	application/pdf	5170	760	76	Fast Nonnegative Matrix Tri-Factorization for Large-Scale Data Co-Clustering Nonnegative Matrix Factorization (NMF) based coclustering methods have attracted increasing attention in recent years because of their mathematical However, the algorithms to solve NMF problems usually involve intensive matrix multiplications, which Moreover, the resulted factor matrices can directly assign cluster labels to data points and features due to the nature of indicator matrices. However, in many real world applications, the clustering based analysis is interested in twoside clustering results, i.e. group the data points and features of NMTF based co-clustering approaches is the slow computational speed because of intensive matrix multiplications In this paper, we propose a novel Fast Nonnegative Matrix Tri-factorization (FNMTF) approach to efficiently conduct co-clustering on largescale data. The latter, simultaneously clustering the rows (features) and the columns (data points) of an input data matrix, In this section, we evaluate the proposed FNMTF and LPFNMTF approaches, and compare them against state-of-theart (co-)clustering methods, including Semi-NMF (SNMF)	./cache/work_sais73suafgozanfdqttn5js4i.pdf	./txt/work_sais73suafgozanfdqttn5js4i.txt