Label consistency-based deep semisupervised NMF for tumor recognition
Tumor has become a hot topic in the field of image processing and pattern recognition.
Gene expression data is an important way to study tumor. Since gene expression data is …
Gene expression data is an important way to study tumor. Since gene expression data is …
On Rank Selection in Non-Negative Matrix Factorization Using Concordance
The choice of the factorization rank of a matrix is critical, eg, in dimensionality reduction,
filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to …
filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to …
Non-negative matrix factorization for dimensionality reduction
J Olaya, C Otman - ITM web of conferences, 2022 - itm-conferences.org
What matrix factorization methods do is reduce the dimensionality of the data without losing
any important information. In this work, we present the Non-negative Matrix Factorization …
any important information. In this work, we present the Non-negative Matrix Factorization …
Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)
MA Tunnell, ZJ DeBruine, E Carrier - arXiv preprint arXiv:2410.14838, 2024 - arxiv.org
Determining the appropriate rank in Non-negative Matrix Factorization (NMF) is a critical
challenge that often requires extensive parameter tuning and domain-specific knowledge …
challenge that often requires extensive parameter tuning and domain-specific knowledge …
[PDF][PDF] Nonnegative Matrix Factorization with Sum-of-Norms Regularization for Hyperspectral Unmixing
WB Hamed - 2022 - uwaterloo.ca
Abstract We introduce a Nonnegative Matrix Factorization (NMF) model with a regularization
function that encourages a low-rank representation of data. We apply our method to …
function that encourages a low-rank representation of data. We apply our method to …