Abstract-Support Vector Machines (SVM) and Nonnegative Matrix Factorization (NMF) are standard tools for data analysis. We explore the connections between these two problems, thereby enabling us to import algorithms from SVM world to solve NMF and vice-versa. In particular, one such algorithm developed to solve SVM is adapted to solve NMF. Empirical results show that this new algorithm is competitive with the state-of-the-art NMF solvers
Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investig...
In the Nonnegative Matrix Factorization (NMF) problem we are given an n×m nonnegative matrix M and a...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract feat...
The dual formulation of the support vector machine (SVM) objective function is an instance of a nonn...
AbstractNonnegative matrix factorization (NMF), the problem of approximating a nonnegative matrix wi...
Nonnegative matrix factorization (NMF) has been success-fully applied to different domains as a tech...
This paper describes a new approach, based on linear programming, for computing nonnegative matrix f...
Nonnegative matrix factorization (NMF) has drawn considerable interest in recent years due to its im...
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n*m matrix ...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Abstract—Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mi...
An exact nonnegative matrix decomposition algorithm is proposed. This is achieved by 1) Taking a non...
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning becau...
Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involv...
Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investig...
In the Nonnegative Matrix Factorization (NMF) problem we are given an n×m nonnegative matrix M and a...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...
Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract feat...
The dual formulation of the support vector machine (SVM) objective function is an instance of a nonn...
AbstractNonnegative matrix factorization (NMF), the problem of approximating a nonnegative matrix wi...
Nonnegative matrix factorization (NMF) has been success-fully applied to different domains as a tech...
This paper describes a new approach, based on linear programming, for computing nonnegative matrix f...
Nonnegative matrix factorization (NMF) has drawn considerable interest in recent years due to its im...
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n*m matrix ...
Abstract Nonnegative Matrix Factorization (NMF) has been proved to be valuable in many ap-plications...
Abstract—Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mi...
An exact nonnegative matrix decomposition algorithm is proposed. This is achieved by 1) Taking a non...
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning becau...
Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involv...
Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investig...
In the Nonnegative Matrix Factorization (NMF) problem we are given an n×m nonnegative matrix M and a...
Nonnegative matrix factorization (NMF) has been successfully used in different applications includin...