Relevant bibliographies by topics / MATRIX FACTORIZATION TECHNIQUES

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'MATRIX FACTORIZATION TECHNIQUES'

Author: Grafiati
Published: 19 January 2024

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Journal articles on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix Factorization Techniques for Recommender Systems." Computer 42, no. 8 (2009): 30–37. http://dx.doi.org/10.1109/mc.2009.263.

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Du, Ke-Lin, M. N. S. Swamy, Zhang-Quan Wang, and Wai Ho Mow. "Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics." Mathematics 11, no. 12 (2023): 2674. http://dx.doi.org/10.3390/math11122674.

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Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary. Compressed sensing, sparse approximation, and dictionary learning are topics similar to sparse coding. Matrix completion is the process of recovering a data matrix from a subset of its entries, and it extends the principles of compressed sensing and sparse approximation. The nonnegative matrix factorization is a low-rank matrix factorization tec
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Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.

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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic
 Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.
 
 Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to
 the users. 
 
 Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF.
 This is because the technique is based on gamma distribution to the model user and item matrix. U
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Nguyen, Jennifer, and Mu Zhu. "Content-boosted matrix factorization techniques for recommender systems." Statistical Analysis and Data Mining 6, no. 4 (2013): 286–301. http://dx.doi.org/10.1002/sam.11184.

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Wang, Fei, Hanghang Tong, and Ching-Yung Lin. "Towards Evolutionary Nonnegative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 501–6. http://dx.doi.org/10.1609/aaai.v25i1.7927.

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Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in
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Khalane, Vivek, Shekhar Suralkar, and Umesh Bhadade. "Image Encryption Based on Matrix Factorization." International Journal of Safety and Security Engineering 10, no. 5 (2020): 655–61. http://dx.doi.org/10.18280/ijsse.100510.

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In this paper, we present a matrix decomposition-based approach for image cryptography. The proposed method consists of decomposing the image into different component and scrambling the components to form the image encryption technique. We use two different type of matrix decomposition techniques to check the efficiency of proposed encryption method. The decomposition techniques used are Independent component analysis (ICA) and Non-Negative Matrix factorization (NMF). The proposed technique has unique user defined parameters (key) such as decomposition method, number of decomposition component
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Nalavade, Jagannath E., Chandra Sekhar Kolli, and Sanjay Nakharu Prasad Kumar. "Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation." Multiagent and Grid Systems 19, no. 2 (2023): 169–85. http://dx.doi.org/10.3233/mgs-230039.

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Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates t
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Tong, Lei, Jing Yu, Chuangbai Xiao, and Bin Qian. "Hyperspectral unmixing via deep matrix factorization." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 06 (2017): 1750058. http://dx.doi.org/10.1142/s0219691317500588.

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Hyperspectral unmixing is one of the most important techniques in hyperspectral remote sensing image analysis. During the past decades, many models have been widely used in hyperspectral unmixing, such as nonnegative matrix factorization (NMF) model, sparse regression model, etc. Most recently, a new matrix factorization model, deep matrix, is proposed and shows good performance in face recognition area. In this paper, we introduce the deep matrix factorization (DMF) for hyperspectral unmixing. In this method, the DMF method is applied for hyperspectral unmixing. Compared with the traditional
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Yashwanth, A. "Audio Enhancement and Denoising using Online Non-Negative Matrix Factorization and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1703–9. http://dx.doi.org/10.22214/ijraset.2022.44061.

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Abstract: For many years, reducing noise in a noisy speech recording has been a difficult task with numerous applications. This gives scope to use better techniques to enhance the audio and speech and to reduce the noise in the audio. One such technique is Online Non-Negative Matrix Factorization (ONMF). ONMF noise reduction approach primarily generates a noiseless audio signal from an audio sample that has been contaminated by additive noise. Previously many approaches were based on nonnegative matrix factorization to spectrogram measurements. Non-negative Matrix Factorization (NMF) is a stan
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Lin, Chih-Jen. "Projected Gradient Methods for Nonnegative Matrix Factorization." Neural Computation 19, no. 10 (2007): 2756–79. http://dx.doi.org/10.1162/neco.2007.19.10.2756.

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Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided.
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Dissertations / Theses on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Frederic, John. "Examination of Initialization Techniques for Nonnegative Matrix Factorization." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/63.

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While much research has been done regarding different Nonnegative Matrix Factorization (NMF) algorithms, less time has been spent looking at initialization techniques. In this thesis, four different initializations are considered. After a brief discussion of NMF, the four initializations are described and each one is independently examined, followed by a comparison of the techniques. Next, each initialization's performance is investigated with respect to the changes in the size of the data set. Finally, a method by which smaller data sets may be used to determine how to treat larger data sets
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Herrmann, Julien. "Memory-aware Algorithms and Scheduling Techniques for Matrix Computattions." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1043/document.

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Dans cette thèse, nous nous sommes penchés d’un point de vue à la foisthéorique et pratique sur la conception d’algorithmes et detechniques d’ordonnancement adaptées aux architectures complexes dessuperordinateurs modernes. Nous nous sommes en particulier intéressésà l’utilisation mémoire et la gestion des communications desalgorithmes pour le calcul haute performance (HPC). Nous avonsexploité l’hétérogénéité des superordinateurs modernes pour améliorerles performances du calcul matriciel. Nous avons étudié lapossibilité d’alterner intelligemment des étapes de factorisation LU(plus rapide) et
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Julià, Ferré Ma Carme. "Missing Data Matrix Factorization Addressing the Structure from Motion Problem." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5785.

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Aquest treball es centra en la factorització de matrius per obtenir l'Estructura a partir del Moviment (SFM). La idea és descomposar la matriu de trajectòries en la matriu de moviment, que conté la posició relativa càmera-objecte a cada frame, i la matriu de forma, que conté les coordenades 3D dels punts característics. Aquesta factorització es pot obtenir utilitzant que la matriu de trajectòries té un rang reduït. En particular, si les trajectòries pertanyen a un únic objecte rígid, la matriu té com a molt rang 4. Tot i que s'han proposat diverses tècniques per tractar el problema de les matr
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Holländer, John. "Investigating the performance of matrix factorization techniques applied on purchase data for recommendation purposes." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20624.

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Automated systems for producing product recommendations to users is a relatively new area within the field of machine learning. Matrix factorization techniques have been studied to a large extent on data consisting of explicit feedback such as ratings, but to a lesser extent on implicit feedback data consisting of for example purchases.The aim of this study is to investigate how well matrix factorization techniques perform compared to other techniques when used for producing recommendations based on purchase data. We conducted experiments on data from an online bookstore as well as an onlin
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Diop, Mamadou. "Décomposition booléenne des tableaux multi-dimensionnels de données binaires : une approche par modèle de mélange post non-linéaire." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0222/document.

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Cette thèse aborde le problème de la décomposition booléenne des tableaux multidimensionnels de données binaires par modèle de mélange post non-linéaire. Dans la première partie, nous introduisons une nouvelle approche pour la factorisation booléenne en matrices binaires (FBMB) fondée sur un modèle de mélange post non-linéaire. Contrairement aux autres méthodes de factorisation de matrices binaires existantes, fondées sur le produit matriciel classique, le modèle proposé est équivalent au modèle booléen de factorisation matricielle lorsque les entrées des facteurs sont exactement binaires et d
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Waggoner, Alexander A. "Triple Non-negative Matrix Factorization Technique for Sentiment Analysis and Topic Modeling." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1550.

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Topic modeling refers to the process of algorithmically sorting documents into categories based on some common relationship between the documents. This common relationship between the documents is considered the “topic” of the documents. Sentiment analysis refers to the process of algorithmically sorting a document into a positive or negative category depending whether this document expresses a positive or negative opinion on its respective topic. In this paper, I consider the open problem of document classification into a topic category, as well as a sentiment category. This has a direct appl
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Dia, Nafissa. "Suivi non-invasif du rythme cardiaque foetal : exploitation de la factorisation non-négative des matrices sur signaux électrocardiographiques et phonocardiographiques." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAS034.

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Avec plus de 200 000 naissances par jour dans le monde, la surveillance du bien-être fœtal pendant l'accouchement est un enjeu clinique majeur. Ce suivi se fait au travers du rythme cardiaque fœtal (RCF) et de sa variabilité, et doit être robuste tout en minimisant le nombre de capteurs non-invasifs sur l'abdomen de la mère.Dans ce contexte, les signaux électrocardiogrammes (ECG) et phonocardiogrammes (PCG) sont d’intérêt, puisqu'ils fournissent tous deux des informations cardiaques, à la fois redondantes et complémentaires. Cette multimodalité ainsi que certaines caractéristiques, telles que
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Filippi, Marc. "Séparation de sources en imagerie nucléaire." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT025/document.

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En imagerie nucléaire (scintigraphie, TEMP, TEP), les diagnostics sont fréquemment faits à l'aide des courbes d'activité temporelles des différents organes et tissus étudiés. Ces courbes représentent l'évolution de la distribution d'un traceur radioactif injecté dans le patient. Leur obtention est compliquée par la superposition des organes et des tissus dans les séquences d'images 2D, et il convient donc de séparer les différentes contributions présentes dans les pixels. Le problème de séparation de sources sous-jacent étant sous-déterminé, nous proposons d'y faire face dans cette thèse en ex
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Pham, Viet Nga. "Programmation DC et DCA pour l'optimisation non convexe/optimisation globale en variables mixtes entières : Codes et Applications." Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00833570.

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Basés sur les outils théoriques et algorithmiques de la programmation DC et DCA, les travaux de recherche dans cette thèse portent sur les approches locales et globales pour l'optimisation non convexe et l'optimisation globale en variables mixtes entières. La thèse comporte 5 chapitres. Le premier chapitre présente les fondements de la programmation DC et DCA, et techniques de Séparation et Evaluation (B&B) (utilisant la technique de relaxation DC pour le calcul des bornes inférieures de la valeur optimale) pour l'optimisation globale. Y figure aussi des résultats concernant la pénalisation ex
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Jiang, Jia-Yun, and 姜佳昀. "Exists or Not: A Differentially Private Matrix Factorization using Randomized Response Techniques." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/58842084091194723748.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>Collaborative filtering (CF) is a popular and widely-used technique for recommendation systems. However, it has privacy concerns of data leakage caused by untrusted servers. To address this problem, we propose a privacy-preserving framework for one of the robustest CF-based method, Matrix Factorization (MF). With the advantage of the characteristic of MF, this framework is based on gradient-transmission client-server architecture to preserve value of feedback and trained model. On basis of this architecture, we further preserve the existence of feedback by a
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Books on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Naik, Ganesh R., ed. Non-negative Matrix Factorization Techniques. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48331-2.

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Symeonidis, Panagiotis, and Andreas Zioupos. Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0.

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Symeonidis, Panagiotis, and Andreas Zioupos. Matrix and Tensor Factorization Techniques for Recommender Systems. Springer London, Limited, 2016.

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Symeonidis, Panagiotis, and Andreas Zioupos. Matrix and Tensor Factorization Techniques for Recommender Systems. Springer, 2017.

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Naik, Ganesh R. Non-negative Matrix Factorization Techniques: Advances in Theory and Applications. Springer, 2016.

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Naik, Ganesh R. Non-Negative Matrix Factorization Techniques: Advances in Theory and Applications. Springer Berlin / Heidelberg, 2015.

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Naik, Ganesh R. Non-Negative Matrix Factorization Techniques: Advances in Theory and Applications. Springer, 2015.

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Book chapters on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Symeonidis, Panagiotis, and Andreas Zioupos. "Related Work on Matrix Factorization." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_2.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Related Work on Tensor Factorization." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_5.

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Balu, Raghavendran, Teddy Furon, and Laurent Amsaleg. "Sketching Techniques for Very Large Matrix Factorization." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30671-1_68.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Experimental Evaluation on Matrix Decomposition Methods." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_4.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Introduction." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_1.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Performing SVD on Matrices and Its Extensions." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_3.

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Symeonidis, Panagiotis, and Andreas Zioupos. "HOSVD on Tensors and Its Extensions." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_6.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Experimental Evaluation on Tensor Decomposition Methods." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_7.

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Symeonidis, Panagiotis, and Andreas Zioupos. "Conclusions and Future Work." In Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_8.

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Chen, Liang, and Peidong Zhu. "Matrix Factorization Approach Based on Temporal Hierarchical Dirichlet Process." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_20.

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Conference papers on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Baviskar, Vishal Shekhar, and K. N. Meera. "Recommender systems: Matrix factorization." In 2ND INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMPUTATIONAL TECHNIQUES. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0148412.

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Balu, Raghavendran, and Teddy Furon. "Differentially Private Matrix Factorization using Sketching Techniques." In IH&MMSec '16: ACM Information Hiding and Multimedia Security Workshop. ACM, 2016. http://dx.doi.org/10.1145/2909827.2930793.

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Baltrunas, Linas, Bernd Ludwig, and Francesco Ricci. "Matrix factorization techniques for context aware recommendation." In the fifth ACM conference. ACM Press, 2011. http://dx.doi.org/10.1145/2043932.2043988.

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Hashemi, Soheil, and Sherief Reda. "Generalized Matrix Factorization Techniques for Approximate Logic Synthesis." In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2019. http://dx.doi.org/10.23919/date.2019.8715274.

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Mehta, Rachana, and Keyur Rana. "A review on matrix factorization techniques in recommender systems." In 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). IEEE, 2017. http://dx.doi.org/10.1109/cscita.2017.8066567.

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Siy, Peter W., Richard A. Moffitt, R. Mitchell Parry, et al. "Matrix factorization techniques for analysis of imaging mass spectrometry data." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering. IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696797.

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Schachtner, R., D. Lutter, A. M. Tome, E. W. Lang, and P. Gomez Vilda. "Exploring Matrix Factorization Techniques for Classification of Gene Expression Profiles." In 2007 IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/wisp.2007.4447571.

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Kalloori, Saikishore, Francesco Ricci, and Marko Tkalcic. "Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques." In RecSys '16: Tenth ACM Conference on Recommender Systems. ACM, 2016. http://dx.doi.org/10.1145/2959100.2959142.

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Qian, Yuntao, Sen Jia, Jun Zhou, and Antonio Robles-Kelly. "L1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing." In 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2010. http://dx.doi.org/10.1109/dicta.2010.82.

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Kong, Wei, Xiaoyang Mou, and Xiaohua Hu. "Exploring matrix factorization techniques for significant genes identification of microarray dataset." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706599.

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Reports on the topic "MATRIX FACTORIZATION TECHNIQUES"

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Baca, L. S., and D. E. Salane. Two classes of preconditioners computed using block matrix factorization techniques. Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/5928153.

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