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Fairer non-negative matrix factorization [PDF]
Frontiers in Big DataThere 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 +5 more
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Collaborative filtering based on nonnegative/binary matrix factorization [PDF]
Frontiers in Big DataCollaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui +5 more
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Deep maximum margin matrix factorization [PDF]
Scientific ReportsCollaborative filtering (CF) over ordinal feedback is naturally organized as a problem of matrix completion, where the input consists of a partially observed user-item interaction matrix. Maximum Margin Matrix Factorization (MMMF) has achieved widespread
Shailendra Kumar +3 more
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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
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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 +5 more
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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 +3 more
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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
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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 +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 +2 more
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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 +3 more
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