Results 11 to 20 of about 208,766 (283)

A Review on Quadrant Interlocking Factorization: WZ andWH Factorization

Journal of Nigerian Society of Physical Sciences, 2023
Quadrant Interlocking Factorization (QIF), an alternative to LU factorization, is suitable for factorizing invertible matrix A such that det(A) , 0.
Dlal Bashir, Hailiza Kamarulhaili, Olayiwola Babarinsa   +2 more
doaj   +1 more source

Coseparable Nonnegative Matrix Factorization

SIAM Journal on Matrix Analysis and Applications, 2023
Nonnegative matrix factorization (NMF) is a popular model in the field of pattern recognition. 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 can be solved efficiently under separability assumption, which requires the columns of factor matrix
Junjun Pan, Michael K. Ng
openaire   +3 more sources

Boolean Matrix Factorization via Nonnegative Auxiliary Optimization

IEEE Access, 2021
A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix whose Boolean structure is ...
Duc P. Truong, Erik Skau, Derek Desantis, Boian Alexandrov   +3 more
doaj   +1 more source

DRaW: prediction of COVID-19 antivirals by deep learning—an objection on using matrix factorization

BMC Bioinformatics, 2023
Background Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved ...
S. Morteza Hashemi, Arash Zabihian, Mohsen Hooshmand, Sajjad Gharaghani   +3 more
doaj   +1 more source

Co-sparse Non-negative Matrix Factorization

Frontiers in Neuroscience, 2022
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu, Jiahui Cai, Canhong Wen, Haizhu Tan   +3 more
doaj   +1 more source

Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

IEEE Access, 2020
To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.
Chenkun Zhang, Cheng Wang
doaj   +1 more source

A multi-attention deep neural network model base on embedding and matrix factorization for recommendation

International Journal of Cognitive Computing in Engineering, 2020
Matrix factorization is a popular method in recommendation system. However, the quality of recommendation algorithm based on matrix decomposition is greatly affected by the sparsity of rating data.
Jing Wang, Lei Liu
doaj   +1 more source

Scalable non-negative matrix tri-factorization

BioData Mining, 2017
Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining.
Andrej Čopar, Marinka žitnik, Blaž Zupan   +2 more
doaj   +1 more source

Graph regularized non-negative matrix factorization with $$L_{2,1}$$ L 2 , 1 norm regularization terms for drug–target interactions prediction

BMC Bioinformatics, 2023
Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of
Junjun Zhang, Minzhu Xie
doaj   +1 more source

Collaborative Filtering Recommendation Algorithm Based on Semi-Autoencoder [PDF]

Jisuanji gongcheng, 2021
To effectively use the user-item interaction history and auxiliary information in recommendation systems,this paper proposes an improved collaborative filtering recommendation algorithm.Based on semi-autoencoder,the features of auxiliary information of ...
ZHANG Haobo, XUE Feng, LIU Kai
doaj   +1 more source