Obtaining Profiles Based on Localized Non-negative Matrix Factorization

【Author】 JIANG Ji-xiang, XU Bao-wen, LU Jian-jiang, ZHOU Xiao-yuDepartment of Computer Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, ChinaJiangsu Institute of Software Quality, Nanjing 210096. Jiangsu, ChinaState Key Laboratory of Software, Wuhan University. Wuhan 430072, Hebei,ChinaSchool of Communications Engineering, People’ s Liberation Army University of Science & Technology, Nanjing 210007, Jiangsu, China

【摘要】 <正> Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper. we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF.更多还原

【Abstract】 Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper. we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF.更多还原