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Fairer non-negative matrix factorization [PDF]

Frontiers in Big Data
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical
Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li   +5 more
doaj   +2 more sources

Collaborative filtering based on nonnegative/binary matrix factorization [PDF]

Frontiers in Big Data
Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura, Kazue Kudo, Kazue Kudo   +5 more
doaj   +2 more sources

Some properties of various types of matrix factorization [PDF]

ITM Web of Conferences, 2021
Matrix factorizations or matrix decompositions are methods that represent a matrix as a product of two or more matrices. There are various types of matrix factorizations such as LU factorization, Cholesky factorization, singular value decomposition etc ...
Ng Wei Shean, Tan Wei Wen
doaj   +1 more source

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation [PDF]

Entropy
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity.
Ling Zhong, Haiyan Gao
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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]

ITM Web of Conferences, 2022
—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 (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
doaj   +1 more source

Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics

Mathematics, 2023
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du, M. N. S. Swamy, Zhang-Quan Wang, Wai Ho Mow   +3 more
doaj   +1 more source

Neural Metric Factorization for Recommendation

Mathematics, 2022
All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization.
Xiaoxin Sun, Liqiu Gong, Zhichao Han, Peng Zhao, Junchao Yu, Suhua Wang   +5 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
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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

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