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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Yunfang Fu Qiuqi Ruan Gaoyun An Yi Jin Institute of Information Science Beijing Jiaotong University School of Computer Science & Engineering Shijiazhuang University Beijing Key Laboratory of Advanced Information Science and Network Technology

In this paper, we utilize 4D tensor structure to explore efficient structure information for 3D facial expression recognition. As a powerful tool to analyze multidimensional nonnegative tensor data, nonnegative tensor factorization(NTF) aims to obtain a partly localized representation of highdimensional tensors. However, the NTF algorithms often suffer from both high computational complexity and memory requirements for high-order tensor data. To overcome these disadvantages, we present a low-rank approximation method to decrease the computation complexity. Meanwhile, we extract local geometric and discriminant information to improve facial recognition. Therefore, we propose fast nonnegative tensor factorization based on graph-preserving(FNTFGP) algorithm for 3D facial expression recognition(FER). It is a trial to explore and apply the proposed method into 3D FER. Experiments are conducted on the BU-3DFE database. The result of experiments shows the validity and efficiency of the proposed approaches. The proposed algorithm opens a promising direction for the higher performance of 3D facial expression recognition.

关键词： 3D facial expression recognition nonnegative tensor factorization low-rank approximation graph preserving neighborhood preserving constraint

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Tag Refinement for User-Contributed Images via Graph Learning and nonnegative tensor factorization

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IEEE SIGNAL PROCESSING LETTERS 2015年第9期22卷 1302-1305页

作者： Qian, Zhiming Zhong, Ping Wang, Runsheng Natl Univ Def Technol Sch Elect Sci & Engn Natl Key Lab Automat Target Recognit ATR Changsha 410073 Hunan Peoples R China

Social image tagging systems mostly suffer from poor performance for image retrieval due to the noisy and incomplete correspondences between user-contributed images and their associated tags. In this letter, we aim to refine tag allocations in the social tagging data provided by these systems. In particular, we propose to harness the tagged and untagged data with a two-stage strategy according to different types of data relations, i.e. item similarity defined by prior knowledge and item co-occurrence learned from data statistics. To solve the sparsity problem, we first introduce a new graph learning (GL) method for enriching the tagging data according to item similarities. Then, we develop a method of nonnegative tensor factorization (NTF) for learning more coherent ternary relations among users, images and tags coupled by the manifold constraints learned from item co-occurrences. Experimental results with the tagging data from the NUS-WIDE dataset have been reported to validate the effectiveness of the proposed method.

关键词： Graph learning image retrieval nonnegative tensor factorization tag refinement

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Variable Selection for Efficient nonnegative tensor factorization 15

Variable Selection for Efficient Nonnegative Tensor Factoriz...

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IEEE International Conference on Data Mining (ICDM)

作者： Kimura, Keigo Kudo, Mineichi Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

ISBN: (纸本)9781467395038

nonnegative tensor factorization (NTF) has become a popular tool for extracting informative patterns from tensor data. However, NTF has high computational cost both in space and in time, mostly in iterative calculation of the gradient. In this paper, we consider variable selection to reduce the cost, assuming sparsity of the factor matrices. In fact, it is known that the factor matrices are often very sparse in many applications such as network analysis, text analysis and image analysis. We update only a small subset of important variables in each iterative step. We show the effectiveness of the algorithm analytically and experimentally in comparison with conventional NTF algorithms. The algorithm was five times faster than the naive algorithm in the best case and required one to five hundred times less memory while keeping the approximation accuracy as the same.

关键词： CANDECOMP/PARAFAC nonnegative tensor factorization tensor factorization

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nonnegative tensor factorization, COMPLETELY POSITIVE tensorS, AND A HIERARCHICAL ELIMINATION ALGORITHM

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SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS 2014年第4期35卷 1227-1241页

作者： Qi, Liqun Xu, Changqing Xu, Yi Hong Kong Polytech Univ Dept Appl Math Kowloon Hong Kong Peoples R China Suzhou Univ Sci & Technol Sch Math & Phys Suzhou 215009 Peoples R China Southeast Univ Dept Math Nanjing 210096 Jiangsu Peoples R China

nonnegative tensor factorization has applications in statistics, computer vision, exploratory multiway data analysis, and blind source separation. A symmetric nonnegative tensor, which has an exact symmetric nonnegative factorization, is called a completely positive tensor. This concept extends the concept of completely positive matrices. A classical result in the theory of completely positive matrices is that a symmetric, diagonally dominated nonnegative matrix is a completely positive matrix. In this paper, we introduce strongly symmetric tensors and show that a symmetric tensor has a symmetric binary decomposition if and only if it is strongly symmetric. Then we show that a strongly symmetric, hierarchically dominated nonnegative tensor is a completely positive tensor, and present a hierarchical elimination algorithm for checking this. Numerical examples are given to illustrate this. Some other properties of completely positive tensors are discussed. In particular, we show that the completely positive tensor cone and the co-positive tensor cone of the same order are dual to each other.

关键词： nonnegative tensor factorization completely positive tensor eigenvalues dominance properties copositive tensor strongly symmetric tensor hierarchical dominance hierarchical elimination algorithm

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Electrical Signal Source Separation Via nonnegative tensor factorization Using On Site Measurements in a Smart Home

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2014年第2期63卷 364-373页

作者： Figueiredo, Marisa Ribeiro, Bernardete de Almeida, Ana Univ Coimbra Ctr Informat & Syst P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Univ Inst Lisbon ISCTE IUL P-1600042 Lisbon Portugal Univ Coimbra Ctr Informat & Syst P-3030290 Coimbra Portugal

Measuring the electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's electrical signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.

关键词： Electrical signal disaggregation nonintrusive load monitoring nonnegative tensor factorization single-channel source separation sparse coding

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Parallel nonnegative tensor factorization via Newton Iteration on Matrices

Parallel Nonnegative Tensor Factorization via Newton Iterati...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Flatz, Markus Vajtersic, Marian Salzburg Univ Dept Comp Sci A-5020 Salzburg Austria Slovak Acad Sci Inst Math Dept Informat Bratislava Slovakia

ISBN: (纸本)9781479953134

nonnegative Matrix factorization (NMF) is a technique to approximate a large nonnegative matrix as a product of two significantly smaller nonnegative matrices. Since matrices can be seen as second-order tensors, NMF can be generalized to nonnegative tensor factorization (NTF). To compute an NTF, the tensor problem can be transformed into a matrix problem by using matricization. Any NMF algorithm can be used to process such a matricized tensor, including a method based on Newton iteration. Here, an approach will be presented to adopt our parallel design of the Newton algorithm for NMF to compute an NTF in parallel for tensors of any order.

关键词： nonnegative tensor factorization Newton Iteration nonnegative Matrix factorization

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Video Forgery Detection Based on nonnegative tensor factorization 4

Video Forgery Detection Based on Nonnegative Tensor Factoriz...

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4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Yin, Liguo Bai, Zhengyao Yang, Renqing Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China

ISBN: (纸本)9781479948086

With the development and popularity of powerful video editing tools, it tends to be increasingly easier to create digital synthesized videos. A novel passive video inter-frame forgery detection method based on nonnegative tensor factorization (NTF) is presented in this work. It is based on the finding that inter-frame forgery will disturb the consistency of time-dimension factor. In this method, the video is factorized by using NTF first and then the time-dimension factor is extracted. By comparing the correlation between the elements of the factor, the video forgery can be detected. According to the experimental results, it shows that the proposed scheme is able to expose frame deletion and insertion forgery effectively.

关键词： video forgery nonnegative tensor factorization frame deletion frame insertion

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Fast nonnegative tensor factorization by Using Accelerated Proximal Gradient

Fast Nonnegative Tensor Factorization by Using Accelerated P...

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11th International Symposium on Neural Networks (ISNN)

作者： Zhou, Guoxu Zhao, Qibin Zhang, Yu Cichocki, Andrzej RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama 3510198 Japan Guangdong Univ Technol Fac Automat Guangzhou Guangdong Peoples R China E China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

ISBN: (纸本)9783319124360;9783319124353

nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, existing algorithms suffer from slow convergence caused by the non-negativity constraint and hence their practical applications are severely limited. By combining accelerated proximal gradient and low-rank approximation, we propose a new NTF algorithm which is significantly faster than state-of-the-art NTF algorithms.

关键词： CP (PARAFAC) decompositions nonnegative tensor factorization Accelerated proximal gradient

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Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using nonnegative Coupled Matrix tensor factorization

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SOCIAL NETWORK ANALYSIS AND MINING 2021年第1期11卷 57页

作者： Balasubramaniam, Thirunavukarasu Nayak, Richi Khanh Luong Abul Bashar, Md Queensland Univ Technol Sch Comp Sci 2 George St Brisbane Qld 4000 Australia Queensland Univ Technol Ctr Data Sci 2 George St Brisbane Qld 4000 Australia

Social media platforms like Twitter have become an easy portal for billions of people to connect and exchange their thoughts. Unfortunately, people commonly use these platforms to share misinformation which can influence other people adversely. The spread of misinformation is unavoidable in an extraordinary situation like Covid-19, and the consequences can be dreadful. This paper proposes a two-step ranking-based misinformation detection (RMiD) technique. Firstly, a novel ranking-based approach leveraging the scalable information retrieval infrastructure is applied to detect misinformation from a huge collection of unlabelled tweets based on a related but very small labelled misinformation data set. Secondly, the identified misinformation tweets are represented as a coupled matrix tensor model and nonnegative Coupled Matrix tensor factorization is applied to learn their spatio-temporal topic dynamics. The experimental analysis shows that RMiD is capable of detecting misinformation with better coverage and less noise in comparison with existing techniques. Moreover, the coupled matrix tensor representation has improved the quality of topics discovered from unlabelled data up to 4% by leveraging the semantic similarity of terms in labelled data.

关键词： Covid-19 Misinformation detection Topic modelling Ranking Spatio-temporal patterns nonnegative tensor factorization Saturating Coordinate Descent

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Video Forgery Detection Based on nonnegative tensor factorization

Video Forgery Detection Based on Nonnegative Tensor Factoriz...

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2014 4th IEEE International Conference on Information Science and Technology

作者： Liguo Yin Zhengyao Bai Renqing Yang School of Information Science and Engineering Yunnan University

With the development and popularity of powerful video editing tools, it tends to be increasingly easier to create digital synthesized videos. A novel passive video inter-frame forgery detection method based on nonnegative tensor factorization(NTF) is presented in this work. It is based on the finding that inter-frame forgery will disturb the consistency of time-dimension factor. In this method, the video is factorized by using NTF first and then the time-dimension factor is extracted. By comparing the correlation between the elements of the factor, the video forgery can be detected. According to the experimental results, it shows that the proposed scheme is able to expose frame deletion and insertion forgery effectively.

关键词： video forgery nonnegative tensor factorization frame deletion frame insertion

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