International audienceIn this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF algorithms are very restricted in the size of tensors that can be decomposed. Our algorithm overcomes this size restriction by interpreting the tensor as a set of sub-tensors and by proceeding the decomposition of sub-tensor by sub-tensor. This approach requires only one sub-tensor at once to be available in memory
Summarization: Most tensor decomposition algorithms were developed for in-memory computation on a si...
Abstract—Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis,...
International audienceIn this letter, the problem of nonnegative tensor decompositions is addressed....
In this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF a...
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor dec...
Abstract—Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-ne...
With the advancements in computing technology and web-based applications, data are increasingly gene...
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust i...
Abstract. In this paper we present a new method of 3D non-negative tensor factorization (NTF) that i...
Non-negative tensor factorization (NTF) has attracted great attention in the machine learning commun...
Non-negative tensor factorization (NTF) has attracted great attention in the machine learning commun...
Abstract. Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient...
Conventional non-negative tensor factorization (NTF) methods assume there is only one tensor that ne...
Non-negative big data arising in many engineering problems may take the form of matrices or multi-di...
Many applications in computer vision, biomedical informatics, and graphics deal with data in the mat...
Summarization: Most tensor decomposition algorithms were developed for in-memory computation on a si...
Abstract—Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis,...
International audienceIn this letter, the problem of nonnegative tensor decompositions is addressed....
In this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF a...
Tensors can be viewed as multilinear arrays or generalizations of the notion of matrices. Tensor dec...
Abstract—Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-ne...
With the advancements in computing technology and web-based applications, data are increasingly gene...
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust i...
Abstract. In this paper we present a new method of 3D non-negative tensor factorization (NTF) that i...
Non-negative tensor factorization (NTF) has attracted great attention in the machine learning commun...
Non-negative tensor factorization (NTF) has attracted great attention in the machine learning commun...
Abstract. Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient...
Conventional non-negative tensor factorization (NTF) methods assume there is only one tensor that ne...
Non-negative big data arising in many engineering problems may take the form of matrices or multi-di...
Many applications in computer vision, biomedical informatics, and graphics deal with data in the mat...
Summarization: Most tensor decomposition algorithms were developed for in-memory computation on a si...
Abstract—Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis,...
International audienceIn this letter, the problem of nonnegative tensor decompositions is addressed....