National Natural Science Foundation of China under grants 61772493 and 61933007; Natural Science Foundation of Chongqing (China) under grant cstc2019jcyjjqX0013; Pioneer Hundred Talents Program of Chinese Academy of Sciences
Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning ...
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of appli...
Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximate...
International audienceMultiplicative update algorithms have encountered a great success to solve opt...
Non-negative matrix factorization (NMF) is useful to find basis information of non-negative data. Cu...
16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Spain, 12-15 December 2016H...
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machin...
Low-rank approximations of data (e. g. based on the Singular Value Decomposition) have proven very u...
An inherently non-negative latent factor model is proposed to extract non-negative latent factors fr...
Non-negative matrix factorization (NMF) is a useful computational method to find basis information o...
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications w...
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of appli...
Abstract Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety...
Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matr...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning ...
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of appli...
Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximate...
International audienceMultiplicative update algorithms have encountered a great success to solve opt...
Non-negative matrix factorization (NMF) is useful to find basis information of non-negative data. Cu...
16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Spain, 12-15 December 2016H...
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machin...
Low-rank approximations of data (e. g. based on the Singular Value Decomposition) have proven very u...
An inherently non-negative latent factor model is proposed to extract non-negative latent factors fr...
Non-negative matrix factorization (NMF) is a useful computational method to find basis information o...
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications w...
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of appli...
Abstract Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety...
Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matr...
Clustering is a fundamental problem in unsupervised and semi-supervised machine learning. Besides cl...
Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning ...
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of appli...
Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximate...