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New Penalized Criteria for Smooth Non-Negative Tensor Factorization With Missing Entries
IEEE Transactions on Signal ProcessingInternational audienceTensor factorization models are widely used in many applied fields such as chemometrics, psychometrics, computer vision or communication networks. Real life data collection is often subject to errors, resulting in missing data. Here
François Roueff, Amaury Durand
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Classification of PolSAR image with non-negative tensor factorization approach
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Polarimetric synthetic aperture radar (PolSAR) is of great importance in the remote sensing, which can be used widely in both civil and military fields. However, existing classification methods cannot effectively utilize the spatial structure information of the SAR data.
Shuiping Gou +4 more
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A high performance hardware architecture for non-negative tensor factorization
Microelectronics Journal, 2019Abstract Non-negative tensor factorization (NTF) algorithm is an emerging method for high-dimensional data analysis, which is applied in many fields such as computer vision, and bioinformatics. This paper presents an effective method to accelerate NTF computations and proposes a corresponding hardware architecture, which consists of multiple ...
Biao Min +3 more
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Non-Negative Tensor Factorization using Alpha and Beta Divergences
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007In this paper we propose new algorithms for 3D tensor decomposition/factorization with many potential applications, especially in multi-way blind source separation (BSS), multidimensional data analysis, and sparse signal/image representations. We derive and compare three classes of algorithms: multiplicative, fixed-point alternating least squares ...
Andrzej Cichocki +4 more
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Deep non-negative tensor factorization with multi-way EMG data
Neural Computing and Applications, 2021Tensor decomposition is widely used in a variety of applications such as data mining, biomedical informatics, neuroscience, and signal processing. In this paper, we propose a deep non-negative tensor factorization (DNTF) model to learn intrinsic and hierarchical structures from multi-way data.
Qi Tan 0001, Pei Yang 0001, Guihua Wen
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Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes
IEEE Transactions on Services Computing, 2022Dynamic relationships are frequently encountered in big data and services computing-related applications, like dynamic data of user-side QoS in Web services. They are modeled into a high-dimensional and sparse (HiDS) tensor, which contain rich knowledge regarding temporal patterns.
Hao Wu 0061, Xin Luo 0001, MengChu Zhou
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Vehicle Recognition using Non-negative Tensor Factorization
Journal of the Institute of Electronics and Information Engineers, 2015The active control of a vehicle based on vehicle recognition is one of key technologies for the intelligent vehicle, and the part-based image representation is necessary to recognize vehicles with only partial shapes of vehicles especially in urban scene where occlusions frequently occur.
Jae Min Ban, Hyunchul Kang
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A Sufficient Condition for the Unique Solution of Non-Negative Tensor Factorization
2007The applications of Non-Negative Tensor Factorization (NNTF) is an important tool for brain wave (EEG) analysis. For it to work efficiently, it is essential for NNTF to have a unique solution. In this paper we give a sufficient condition for NNTF to have a unique global optimal solution.
Toshio Sumi, Toshio Sakata
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Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014
People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space ...
Zipei Fan +2 more
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People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space ...
Zipei Fan +2 more
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Affective Color Palette Recommendations with Non-negative Tensor Factorization
2022 26th International Conference Information Visualisation (IV), 2022Ikuya Morita +3 more
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