Results 21 to 30 of about 23,872 (265)

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
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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
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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 Ng 0001
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Randomized nonnegative matrix factorization [PDF]

Pattern Recognition Letters, 2018
This is an extended and revised version of the paper which appeared in ...
N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz   +3 more
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Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering

International Journal of Computational Intelligence Systems, 2020
Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion.
Xiangguang Dai, Nian Zhang, Keke Zhang, Jiang Xiong   +3 more
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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
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Spherical Matrix Factorization

CoRR, 2021
12 pages, 6 figures.
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Deep matrix factorizations

CoRR, 2020
Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way. However, such low-rank approaches are unable to mine complex, interleaved features that underlie hierarchical semantics. Recently, deep matrix
Pierre De Handschutter, Nicolas Gillis, Xavier Siebert   +2 more
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Robust Probabilistic Matrix Tri-factorization

Jisuanji kexue yu tansuo, 2020
Matrix factorization is a commonly-used data analysis tool in computer vision, machine learning and data mining. In recent years, the probabilistic models of matrix factorization have become the focus of attention.
SHI Jiarong, CHEN Jiaojiao
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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
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