IEEE

On Rank Selection for Nonnegative Matrix Factorization

Abstract: Rank selection, i.e. the choice of factorization rank, is the first step in constructing Nonnegative Matrix Factorization (NMF) models. It is a long-standing problem which is not unique to ...
IEEE

Orthogonal Symmetric Nonnegative Matrix Factorization With Low-Rank Tensor Representation for Multilayer Network Community Detection

Abstract: Multilayer networks community detection plays an important role in data mining. It can discover the latent representations of network structures for effectively completing downstream tasks.
University of Waterloo

Nonnegative matrix factorization via rank-one downdating

Biggs, M. , Ghodsi, A. , & Vavasis, S. A. . (2008). Nonnegative matrix factorization via rank-one downdating. In Proceedings of the 2008 International Conference on ...
University of Waterloo

High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition

High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition Citation: Wu, Y., Qin, Y. & Zhu, M., 2020. High-dimensional covariance matrix estimation using a low-rank and ...
GitHub

SingLoRA: Low Rank Adaptation Using a Single Matrix, David Bensaïd+, arXiv'25

Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient fine-tuning of large pretrained models. LoRA augments the pre-trained weights of a model by adding the product of two smaller ...
JSTOR Daily

High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition

We study high-dimensional covariance/precision matrix estimation under the assumption that the covariance/precision matrix can be decomposed into a low-rank component L and a diagonal component D. The ...