Relevant bibliographies by topics / Coupled matrix factorization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Coupled matrix factorization'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Coupled matrix factorization"

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Wei, Chuyuan, Fangfang Li, Xiongzhong Fan, and Qiang Zhan. "Coupled Matrix Factorization for Question Similarity." Chinese Journal of Electronics 25, no. 4 (2016): 665–71. http://dx.doi.org/10.1049/cje.2016.06.034.

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Ermiş, Beyza, and A. Taylan Cemgİl. "Data Sharing via Differentially Private Coupled Matrix Factorization." ACM Transactions on Knowledge Discovery from Data 14, no. 3 (2020): 1–27. http://dx.doi.org/10.1145/3372408.

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Wu, Qing, Jie Wang, Jin Fan, et al. "Improved Coupled Tensor Factorization with Its Applications in Health Data Analysis." Complexity 2019 (February 5, 2019): 1–16. http://dx.doi.org/10.1155/2019/1574240.

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Coupled matrix and tensor factorizations have been successfully used in many data fusion scenarios where datasets are assumed to be exactly coupled. However, in the real world, not all the datasets share the same factor matrices, which makes joint analysis of multiple heterogeneous sources challenging. For this reason, approximate coupling or partial coupling is widely used in real-world data fusion, with exact coupling as a special case of these techniques. However, to fully address the challenge of tensor factorization, in this paper, we propose two improved coupled tensor factorization meth
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Xiao, Ding, Yugang Ji, Yitong Li, Fuzhen Zhuang, and Chuan Shi. "Coupled matrix factorization and topic modeling for aspect mining." Information Processing & Management 54, no. 6 (2018): 861–73. http://dx.doi.org/10.1016/j.ipm.2018.05.002.

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Yang, Yi, Lixin Han, Zhinan Gou, Baobin Duan, Jun Zhu, and Hong Yan. "Tagrec-CMTF: Coupled Matrix and Tensor Factorization for Tag Recommendation." IEEE Access 6 (2018): 64142–52. http://dx.doi.org/10.1109/access.2018.2877764.

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Li, Heng-Chao, Shuang Liu, Xin-Ru Feng, and Shao-Quan Zhang. "Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 5061–73. http://dx.doi.org/10.1109/jstars.2020.3019706.

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Bracken, Paul. "Factorization of second-order matrix differential operators and a matrix Darboux transformation." Canadian Journal of Physics 81, no. 8 (2003): 977–88. http://dx.doi.org/10.1139/p03-076.

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It is shown that a class of matrix Schrödinger operators can be factored into a product of two first-order matrix operators. The equations that relate the elements in these first-order operators to the elements of the potential matrix of the Schrödinger operator are obtained. They are found to be coupled first-order differential equations in the variables of the first-order matrix operators. Finally, an example of a factorization of a matrix operator is obtained, and a general solution associated to a value of the spectral parameter is given. PACS Nos.: 02.30.Mq, 12.39.Pn
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Yokoya, Naoto, Takehisa Yairi, and Akira Iwasaki. "Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 50, no. 2 (2012): 528–37. http://dx.doi.org/10.1109/tgrs.2011.2161320.

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Acar, Evrim, Gozde Gurdeniz, Morten A. Rasmussen, Daniela Rago, Lars O. Dragsted, and Rasmus Bro. "Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics." International Journal of Knowledge Discovery in Bioinformatics 3, no. 3 (2012): 22–43. http://dx.doi.org/10.4018/jkdb.2012070102.

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Metabolomics focuses on the detection of chemical substances in biological fluids such as urine and blood using a number of analytical techniques including Nuclear Magnetic Resonance (NMR) spectroscopy and Liquid Chromatography-Mass Spectrometry (LC-MS). Among the major challenges in analysis of metabolomics data are (i) joint analysis of data from multiple platforms, and (ii) capturing easily interpretable underlying patterns, which could be further utilized for biomarker discovery. In order to address these challenges, the authors formulate joint analysis of data from multiple platforms as a
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Zare, Marzieh, Mohammad Sadegh Helfroush, Kamran Kazemi, and Paul Scheunders. "Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition." Remote Sensing 13, no. 15 (2021): 2930. http://dx.doi.org/10.3390/rs13152930.

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Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-s
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Dissertations / Theses on the topic "Coupled matrix factorization"

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Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.

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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendatio
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Winck, Ryder Christian. "Simultaneous control of coupled actuators using singular value decomposition and semi-nonnegative matrix factorization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45907.

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This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which
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Ingverud, Patrik. "Complexity evaluation of CNNs in tightly coupled hybrid recommender systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232027.

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In this report we evaluated how the complexity of a Convolutional Neural Network (CNN), in terms of number of filters, size of filters and dropout, affects the performance on the rating prediction accuracy in a tightly coupled hybrid recommender system. We also evaluated the effect on the rating prediction accuracy for pretrained CNNs in comparison to non-pretrained CNNs. We found that a less complex model, i.e. smaller filters and less number of filters, showed trends of better performance. Less regularization, in terms of dropout, had trends of better performance for the less complex models.
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Hsieh, Chih-Hsiang, and 謝智翔. "A Convex Optimization Based Coupled Non-negative Matrix Factorization Algorithm for Hyperspectral Image Super-resolution." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/megg23.

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碩士<br>國立清華大學<br>通訊工程研究所<br>106<br>In recent years, fusing a low-spatial-resolution hyperspectral image with a highspatial-resolution multispectral image has been thought of as an economical approach for obtaining high-spatial-resolution hyperspectral image. A fusion criterion, termed coupled nonnegative matrix factorization (CNMF) has been reported to be effective in yielding promising fusion performance. However, the CNMF criterion amounts to an ill-posed inverse problem. In this thesis, we propose a new data fusion algorithm by suitable regularization that significantly outperforms the unreg
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Book chapters on the topic "Coupled matrix factorization"

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Li, Fangfang, Guandong Xu, and Longbing Cao. "Coupled Item-Based Matrix Factorization." In Web Information Systems Engineering – WISE 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11749-2_1.

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Li, Fangfang, Guandong Xu, and Longbing Cao. "Coupled Matrix Factorization Within Non-IID Context." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18032-8_55.

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Jiang, Bo, Feng Yi, Jianjun Wu, and Zhigang Lu. "Retweet Prediction Using Context-Aware Coupled Matrix-Tensor Factorization." In Knowledge Science, Engineering and Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_17.

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Li, Fangfang, Guandong Xu, Longbing Cao, Xiaozhong Fan, and Zhendong Niu. "CGMF: Coupled Group-Based Matrix Factorization for Recommender System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41230-1_16.

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Balasubramaniam, Thirunavukarasu, Richi Nayak, and Chau Yuen. "Nonnegative Coupled Matrix Tensor Factorization for Smart City Spatiotemporal Pattern Mining." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0_44.

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Conference papers on the topic "Coupled matrix factorization"

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Zhang, Liying, Longbing Cao, Sheng Luo, Lei Gu, Yijin Chen, and Yuanfeng Lian. "Coupled Collective Matrix Factorization." In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2018. http://dx.doi.org/10.1109/smartworld.2018.00179.

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Turkmen, Ali Caner, and Ali Taylan Cemgil. "Text classification with coupled matrix factorization." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7496085.

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Zhao, Xueci, Chengzhang Zhu, and Lizhi Cheng. "Coupled Bayesian Matrix Factorization in Recommender Systems." In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2017. http://dx.doi.org/10.1109/dsaa.2017.9.

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Jeon, ByungSoo, Inah Jeon, Lee Sael, and U. Kang. "SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498292.

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Boudehane, Abdelhak, Yassine Zniyed, Arthur Tenenhaus, Laurent Le Brusquet, and Remy Boyer. "Breaking the Curse of Dimensionality for Coupled Matrix-Tensor Factorization." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022462.

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Thirunavukarasu, Balasubramaniam, Nayak Richi, and Chau Yuen. "People to People Recommendation using Coupled Nonnegative Boolean Matrix Factorization." In 2018 International Conference on Soft-computing and Network Security (ICSNS). IEEE, 2018. http://dx.doi.org/10.1109/icsns.2018.8573623.

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Simsekli, Umut, Ali Taylan Cemgil, and Beyza Ermis. "Learning mixed divergences in coupled matrix and tensor factorization models." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178345.

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Wang, Chao, Yang Zhao, Jihong Pei, and Hao Chen. "Coupled non-negative matrix factorization for low-resolution face recognition." In 14th International FLINS Conference (FLINS 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811223334_0176.

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Schenker, Carla, Jeremy E. Cohen, and Evrim Acar. "An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287459.

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Erturk, Alp. "Coupled Nonnegative Matrix Factorization With Local Neighborhood Weights For Data Fusion." In 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105321.

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