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Semantic sensitive tensor factorization
Artificial Intelligence, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nakatsuji, Makoto +4 more
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Convolutional Dictionary Learning through Tensor Factorization [PDF]
, 2015 Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning.
Anandkumar, Animashree, Huang, Furong
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Tensor Factorization via Matrix Factorization
, 2015 Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensor factorization have not reached the level of maturity of matrix factorization methods.
Kuleshov, Volodymyr +2 more
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Understanding International Migration using Tensor Factorization [PDF]
Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion, 2017 Understanding human migration is of great interest to demographers and social scientists. User generated digital data has made it easier to study such patterns at a global scale. Geo coded Twitter data, in particular, has been shown to be a promising source to analyse large scale human migration.
Nguyen, Hieu, Garimella, Kiran
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang +3 more
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Bayesian learning of joint distributions of objects [PDF]
, 2013 There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework
Banerjee, Anjishnu +2 more
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A Biased Deep Tensor Factorization Network For Tensor Completion
, 2021 Tensor decomposition is a popular technique for tensor completion, However most of the existing methods are based on linear or shallow model, when the data tensor becomes large and the observation data is very small, it is prone to over fitting and the performance decreases significantly.
Wu, Qianxi, Xu, An-Bao
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Twist-3 contributions in semi-inclusive DIS with transversely polarized target
Physics Letters B, 2016 We study semi-inclusive DIS with a transversely polarized target in the approach of collinear factorization. The effects related to the transverse polarization are at twist-3.
A.P. Chen, J.P. Ma, G.P. Zhang
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Realization of tensor product and of tensor factorization of rational functions [PDF]
Quantum Studies: Mathematics and Foundations, 2019 We here first study the state space realization of a tensor-product of a pair of rational functions. At the expense of "inflating" the dimensions, we recover the classical expressions for realization of a regular product of rational functions. Then, under an additional assumption that the limit at infinity of a given rational function exists and is ...
Daniel Alpay, Izchak Lewkowicz
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Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting
, 2017 Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require matrix-inversion; and (ii ...
Kasai, Takuma, Ono, Shunsuke
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