Results 11 to 20 of about 75,216 (280)
Towards toric absolute factorization [PDF]
Journal of Symbolic Computation, 2009 This article gives an algorithm to recover the absolute factorization of a bivariate polynomial, taking into account the geometry of its monomials. It is based on algebraic criterions inherited from algebraic interpolation and toric geometry.
Elkadi, M, Galligo, A, Weimann, M
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MJNMF-GAT: Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network for Understanding Adolescent Neurodevelopment [PDF]
IEEE AccessIntegration of multi-task brain imaging data is crucial for furthering our understanding of neurodevelopment. By utilizing functional magnetic resonance imaging (fMRI) data and machine learning methods such as graph neural networks to measure brain ...
Binish Patel +4 more
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Discrete matrix factorization cross-modal hashing with multi-similarity consistency
Complex & Intelligent Systems, 2022 Recently, matrix factorization-based hashing has gained wide attention because of its strong subspace learning ability and high search efficiency. However, some problems need to be further addressed.
Yiru Li +6 more
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Recommendation System Algorithms on Location-Based Social Networks: Comparative Study
Information, 2022 Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content ...
Abeer Al-Nafjan +2 more
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Sparse Non-negative Matrix Factorization Algorithm Based on Proximal Alternating Linearized Minimization [PDF]
Jisuanji gongcheng, 2019 This paper combinessparsity constraint and Proximal Alternating Linearized Minimization(PALM),proposes a Sparse Non-negative Matrix Factorization(SNMF) algorithm,called SNMF_PALM.The non-convex Smoothly Clipped Absolute Deviation(SCAD) function is used ...
WANG Jing,YANG Dan
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IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust
Mathematics, 2022 It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems.
Jun Wu +7 more
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Correlation and realization of quasi-Newton methods of absolute optimization [PDF]
Компьютерные исследования и моделирование, 2016 Newton and quasi-Newton methods of absolute optimization based on Cholesky factorization with adaptive step and finite difference approximation of the first and the second derivatives.
Anastasiya Borisovna Sviridenko +1 more
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IEEE Access, 2020 This study proposes a three-dimensional (3-D) absolute attitude reconstruction algorithm of a rigid body target based on multi-station high-resolution range profile (HRRP) sequences.
Nan Su +3 more
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Remote Sensing, 2022 GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc.
Hanlin Liu, Linchao Li
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Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering
Complexity, 2021 Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems.
Zhengjin Zhang +6 more
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