summary:The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method. We present a Proximal ANLS (PANLS) algorithm to enforce convergence. To speed up the PANLS method, we propose to combine it with a periodic enhanced line search strategy. The resulting algorithm, PANLS/PELS, converges to a critical point of the nonnegative tensor factorization problem under mild conditions. We also provide some numerical results comparing the ANLS and PANLS/PELS methods
Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extrac...
It is well-known that good initializations can improve the speed and accuracy of the solutions of ma...
Summarization: We propose a general algorithmic framework for constrained matrix and tensor factoriz...
summary:The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnega...
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast develop...
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an ...
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an ...
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor dec...
Abstract. Nonnegative matrix factorization has been widely applied in face recognition, text mining,...
Abstract. Tensor factorizations with nonnegative constraints have found application in ana-lyzing da...
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) is a data analysis technique used in a great variety of appli...
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust i...
In this paper, we discuss the acceleration of the regularized alternating least-squares (RALS) algor...
Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extrac...
It is well-known that good initializations can improve the speed and accuracy of the solutions of ma...
Summarization: We propose a general algorithmic framework for constrained matrix and tensor factoriz...
summary:The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnega...
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast develop...
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an ...
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an ...
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor dec...
Abstract. Nonnegative matrix factorization has been widely applied in face recognition, text mining,...
Abstract. Tensor factorizations with nonnegative constraints have found application in ana-lyzing da...
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) is a data analysis technique used in a great variety of appli...
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust i...
In this paper, we discuss the acceleration of the regularized alternating least-squares (RALS) algor...
Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extrac...
It is well-known that good initializations can improve the speed and accuracy of the solutions of ma...
Summarization: We propose a general algorithmic framework for constrained matrix and tensor factoriz...