Results 11 to 20 of about 207,171 (287)

Randomized Nonnegative Matrix Factorization [PDF]

Pattern Recognition Letters, 2018
Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin, Kutz, J. Nathan, Mendible, Ariana, Wihlborn, Sophie   +3 more
core   +2 more sources

Deviance matrix factorization

Electronic Journal of Statistics, 2023
We investigate a general matrix factorization for deviance-based data losses, extending the ubiquitous singular value decomposition beyond squared error loss. While similar approaches have been explored before, our method leverages classical statistical methodology from generalized linear models (GLMs) and provides an efficient algorithm that is ...
Wang, Liang, Carvalho, Luis
openaire   +2 more sources

Linked Matrix Factorization [PDF]

Biometrics, 2018
AbstractSeveral recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set
Michael J. O'Connell, Eric F. Lock
openaire   +3 more sources

Zipf Matrix Factorization: Matrix Factorization with Matthew Effect Reduction [PDF]

2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2021
Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew
openaire   +2 more sources

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

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

Empirical Bayes Matrix Factorization

Journal of machine learning research : JMLR, 2018
Matrix factorization methods - including Factor analysis (FA), and Principal Components Analysis (PCA) - are widely used for inferring and summarizing structure in multivariate data. Many matrix factorization methods exist, corresponding to different assumptions on the elements of the underlying matrix factors.
Wang, Wei, Stephens, Matthew
openaire   +4 more sources