5 days ago · Non-negative matrix factorization methods include blind hyperspectral unmixing with spatial sparsity regularization [13]; endmember constraints and total ...

4 days ago · (3) Approaches based on data decomposition can be categorized into Matrix Factorization, Dictionary-based/Sparse Representation, and Tensor Representation. The ...

4 days ago · This paper addresses the problem of sparse recovery with graph constraints in the sense that we can take additive measurements over nodes only if they induce a ...

7 days ago · We study Higgs production through weak boson fusion with subsequent decay to bottom quarks. By combining jet substructure techniques and matrix element methods ...

1 day ago · Sinkhorn divergence [23] and entropic regularization OT from empirical data. Debiased Sinkhorn barycenters Sinkhorn divergence barycenter [37]; Smooth optimal ...

6 days ago · A low-rank tensor decomposition model that is regularized by the spatial-spectral graph for HSI-MSI fusion is proposed in [68]. Kanatsoulis et al. [22] ...

1 day ago · Therefore, in this work, we propose a novel mixed noise removal model that combines a deterministic low-rankness prior and an implicit regularization scheme. In ...

8 hours ago · ... Nonnegative Matrix Factorization (MNMF) is constructed based on the Nonnegative Matrix Factorization (NMF) with adding the reweighted sparse constraint equation ...