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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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Credit Card Approval Prediction by Non-negative Tensor Factorization
2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021With the increasing number of credit card applications, banks are opting towards the use of prediction-based algorithms as opposed to manual approval methods. Data analysis has exhibited a strong correlation between several financial and personal factors of a client and the likelihood of said client complying with their respective bank's credit ...
Zaima Zarnaz +2 more
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Non-negative Tri-factor tensor decomposition with applications
Knowledge and Information Systems, 2012Non-negative matrix factorization (NMF) mainly focuses on the hidden pattern discovery behind a series of vectors for two-way data. Here, we propose a tensor decomposition model Tri-ONTD to analyze three-way data. The model aims to discover the common characteristics of a series of matrices and at the same time identify the peculiarity of each matrix ...
Zhong-Yuan Zhang, Tao Li, Chris Ding
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Multichannel audio upmixing based on non-negative tensor factorization representation
2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011This paper proposes a new spatial audio coding (SAC) method that is based on parametrization of multichannel audio by sound objects using non-negative tensor factorization (NTF). The spatial parameters are estimated using perceptually motivated NTF model and are used for upmixing a downmixed and encoded mixture signal.
J. Nikunen, T. Virtanen, M. Vilermo
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Tensor Independent Component Analysis and Tensor Non-Negative Factorization
2009In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches.
David Zhang +3 more
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Controlling Sparseness in Non-negative Tensor Factorization
2006Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of the tensor factorization has been empirically observed in many cases, but there was no systematic way to control it.
Matthias Heiler, Christoph Schnörr
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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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FacetCube: a general framework for non-negative tensor factorization
Knowledge and Information Systems, 2012Non-negative tensor factorization (NTF) has been successfully used to extract significant characteristics from polyadic data, such as data in social networks. Because these polyadic data have multiple dimensions (e.g., the author, content, and timestamp of a blog post), NTF fits in naturally and extracts data characteristics jointly from different data
Yun Chi, Shenghuo Zhu
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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, Pei Yang, Guihua Wen
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Spatial feature extraction non-negative tensor factorization for hyperspectral unmixing
Applied Mathematical Modelling, 2022Abstract Estimating endmembers and corresponding abundances from mixed pixels are essential steps for hyperspectral unmixing. In hyperspectral unmixing, obtaining accurate unmixing results is difficult since less prior knowledge is available. Besides, the unmixing results are influenced by noise and highly correlated endmembers, so that the obtained ...
Jin-Ju Wang +3 more
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