Jan 30, 2024 · Abstract:Nonnegative Matrix Factorization (NMF) is an important unsupervised learning method to extract meaningful features from data.

Abstract. Nonnegative matrix factorization (NMF) is an important unsupervised learning method to extract meaningful features from data.

This approach extends the coseparable NMF to the tensor setting, creating what we term coseparable nonnegative tensor factorization (NTF). In this work, we ...

This approach extends the coseparable NMF to the tensor setting, creating what we term coseparable Nonnegative Tensor Factorization (NTF). In this work, we ...

Oct 22, 2024 · We present two forms of the tensor CUR decomposition and deduce the errors ... [Show full abstract] of the approximation. We illustrate how the ...

This paper proposes an extension of coseparable NMF to the tensor (multidimensional array) setting, called coseparable Nonnegative Tensor Factorization (NTF).

It aims to find a low rank approximation for nonnegative data M by a product of two nonnegative matrices W and H. In general, NMF is NP-hard to solve while it ...

Pioneering the extension from matrices to tensors in data analysis, the paper Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition presents ...

Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition ... This approach extends the coseparable NMF to the tensor setting, creating what we term ...

Coseparable Nonnegative Tensor Factorization with t-CUR Decomposition. 协同可分离非负张量分解与t-CUR分解. Juefei Chen, Longxiu Huang, Yimin Wei. DOI: 10.1137 ...