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Опубліковано: 19 січня 2024

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Статті в журналах з теми "MATRIX FACTORIZATION TECHNIQUES"

1

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

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2

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 (June 12, 2023): 2674. http://dx.doi.org/10.3390/math11122674.

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Анотація:
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 technique for nonnegative data. All of these low-rank matrix factorization techniques are unsupervised learning techniques, and can be used for data analysis tasks, such as dimension reduction, feature extraction, blind source separation, data compression, and knowledge discovery. In this paper, we survey a few emerging matrix factorization techniques that are receiving wide attention in machine learning, signal processing, and statistics. The treated topics are compressed sensing, dictionary learning, sparse representation, matrix completion and matrix recovery, nonnegative matrix factorization, the Nyström method, and CUR matrix decomposition in the machine learning framework. Some related topics, such as matrix factorization using metaheuristics or neurodynamics, are also introduced. A few topics are suggested for future investigation in this article.
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3

Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (January 11, 2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.

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Анотація:
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. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well. Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile. Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models. Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.
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4

Nguyen, Jennifer, and Mu Zhu. "Content-boosted matrix factorization techniques for recommender systems." Statistical Analysis and Data Mining 6, no. 4 (April 2, 2013): 286–301. http://dx.doi.org/10.1002/sam.11184.

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5

Wang, Fei, Hanghang Tong, and Ching-Yung Lin. "Towards Evolutionary Nonnegative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 501–6. http://dx.doi.org/10.1609/aaai.v25i1.7927.

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Анотація:
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 a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.
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6

Khalane, Vivek, Shekhar Suralkar, and Umesh Bhadade. "Image Encryption Based on Matrix Factorization." International Journal of Safety and Security Engineering 10, no. 5 (November 30, 2020): 655–61. http://dx.doi.org/10.18280/ijsse.100510.

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Анотація:
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 components and order in which the components are arranged. The unique encryption technique is designed on the basis of these key parameters. The original image can be reconstructed at the decryption end only if the selected parameters are known to the user. The design examples for both decomposition approaches are presented for illustration purpose. We analyze the complexity and encryption time of cryptography system. Results prove that the proposed scheme is more secure as it has less correlation between the input image and the encrypted version of the same as compared to state-of-art methods. The computation time of the proposed approach is found to be comparable.
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7

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 (October 6, 2023): 169–85. http://dx.doi.org/10.3233/mgs-230039.

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Анотація:
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 the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively.
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8

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 (November 2017): 1750058. http://dx.doi.org/10.1142/s0219691317500588.

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Анотація:
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 NMF-based unmixing methods, DMF could extract more information with multiple-layer structures. An optimization algorithm is also proposed for DMF with two designed processes. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-the-art unmixing approaches.
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9

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 (June 30, 2022): 1703–9. http://dx.doi.org/10.22214/ijraset.2022.44061.

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Анотація:
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 standard tool for audio source separation. One major disadvantage of applying NMF on datasets that are large is the time complexity. In this work, we proposed using Online Non-Negative Matrix Factorization. The data can be taken as any speech or music. This method uses less memory than regular non-negative matrix factorization, and it could be used for real-time denoising. This ONMF algorithm is more efficient in memory and time complexity for updates in the dictionary. We have shown that the ONMF method is faster and more efficient for small audio signals on audio simulations. We also implemented this using the Deep Learning approach for comparative study with the Online Non-Negative Matrix Factorization.
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10

Lin, Chih-Jen. "Projected Gradient Methods for Nonnegative Matrix Factorization." Neural Computation 19, no. 10 (October 2007): 2756–79. http://dx.doi.org/10.1162/neco.2007.19.10.2756.

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Анотація:
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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Більше джерел

Дисертації з теми "MATRIX FACTORIZATION TECHNIQUES"

1

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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Анотація:
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 is examined.
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2

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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Анотація:
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 des étapes de factorisation QR (plus stablenumériquement mais plus deux fois plus coûteuses) pour résoudre unsystème linéaire dense. Nous avons amélioré les performances desystèmes d’exécution dynamique à l’aide de pré-calculs statiquesprenants en compte l’ensemble du graphe de tâches de la factorisationCholesky ainsi que l’hétérogénéité de l’architecture. Nous noussommes intéressés à la complexité du problème d’ordonnancement degraphes de tâches utilisant de gros fichiers d’entrée et de sortiesur une architecture hétérogène avec deux types de ressources,utilisant chacune une mémoire spécifique. Nous avons conçu denombreuses heuristiques en temps polynomial pour la résolution deproblèmes généraux que l’on avait prouvés NP-complet aupréalable. Enfin, nous avons conçu des algorithmes optimaux pourordonnancer un graphe de différentiation automatique sur uneplateforme avec deux types de mémoire : une mémoire gratuite maislimitée et une mémoire coûteuse mais illimitée
Throughout this thesis, we have designed memory-aware algorithms and scheduling techniques suitedfor modern memory architectures. We have shown special interest in improving the performance ofmatrix computations on multiple levels. At a high level, we have introduced new numerical algorithmsfor solving linear systems on large distributed platforms. Most of the time, these linear solvers rely onruntime systems to handle resources allocation and data management. We also focused on improving thedynamic schedulers embedded in these runtime systems by adding static information to their decisionprocess. We proposed new memory-aware dynamic heuristics to schedule workflows, that could beimplemented in such runtime systems.Altogether, we have dealt with multiple state-of-the-art factorization algorithms used to solve linearsystems, like the LU, QR and Cholesky factorizations. We targeted different platforms ranging frommulticore processors to distributed memory clusters, and worked with several reference runtime systemstailored for these architectures, such as P A RSEC and StarPU. On a theoretical side, we took specialcare of modelling convoluted hierarchical memory architectures. We have classified the problems thatare arising when dealing with these storage platforms. We have designed many efficient polynomial-timeheuristics on general problems that had been shown NP-complete beforehand
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3

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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Анотація:
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 matrius amb forats, aquestes poden no donar resultats correctes quan el percentatge de forats a la matriu és molt gran. Proposem un esquema multiresolució iteratiu per poder tractar aquests casos amb molts elements buits a la matriu de trajectòries. L'esquema proposat consisteix en considerar submatrius amb un percentatge de forats reduït. Seguidament, els forats d'aquestes matrius són emplenats aplicant un mètode de factorització. L'objectiu final és obtenir millors resultats aplicant un mètode de factorització a la matriu emplenada amb l'esquema proposat, enlloc d'aplicar-lo a la matriu inicial, que presenta un elevat percentatge de forats.
En el cas de matrius de trajectòries corresponents a punts característics que pertanyen a diversos objectes, les tècniques de factorització no es poden aplicar directament per
obtenir el moviment i la forma de cada objecte, ja que les trajectòries no estan ordenades per objectes. A més a més, s'ha de tenir en compte un altre problema: l'estimació del rang de la matriu de trajectòries. El problema és que amb forats, el rang de la matriu no pot ser calculat directament. Per altra banda, com que hi ha múltiples objectes, és difícil d'estimar-lo, sense utilitzar informació com ara nombre d'objectes o tipus de moviment d'aquests. Presentem una tècnica per estimar el rang d'una matriu de trajectòries amb forats. La idea és que, si les trajectòries pertanyen a objectes rígids, la freqüència espectral de la matriu de trajectòries inicial no hauria de variar un cop la matriu ha estat emplenada. Els forats de la matriu són emplenats amb un mètode de factorització, considerant diferents valors per al rang de la matriu. Al mateix temps, el rang de la matriu de trajectòries és estimat fent servir una mesura que compara la freqüència espectral de cada matriu emplenada amb la de la matriu inicial. El proper pas consisteix en segmentar les trajectòries segons el seu moviment. Finalment, qualsevol tècnica d'Estructura a partir de Moviment per a un únic objecte pot ser aplicada per trobar el moviment i la forma de cada objecte.
Intentem aplicar la metodologia proposada per al problema de l'Estructura a partir de Moviment a d'altres aplicacions, no només dins el camp de la visió per computador. En particular, l'objectiu és adaptar els mètodes Alternats per poder aplicar-los a diferents problemes de dimensionalitat reduïda. Una de les possibles aplicacions és la fotometria: la idea és recuperar la reflectància i les normals a la superfície i la direcció de la llum en cada imatge, a partir d'imatges obtingudes sota diferents condicions de llum. En una segona aplicació, l'objectiu és adaptar els mètodes Alternats per poder omplir els forats en una matriu de dades provinents d'expressions de gens. Aquestes matrius són generades amb la informació que proporcionen els DNA microarrays. Finalment, els mètodes Alternats són aplicats a matrius de dades de sistemes de recomanació, molt usats en E-commerce. Aquestes matrius contenen puntuacions que els usuaris han donat a certs productes. La idea és predir les puntuacions que un usuari concret donaria a altres productes, utilitzant la informació emmagatzemada en el sistema.
This work is focused on the missing data matrix factorization addressing the Structure from Motion (SFM) problem. The aim is to decompose a matrix of feature point trajectories into the motion and shape matrices, which contain the relative camera-object motion and the 3D positions of tracked feature points, respectively. This decomposition can be found by using the fact that the matrix of trajectories has a reduced rank. Although several techniques have been proposed to tackle this problem, they may give undesirable results when the percentage of missing data is high. An iterative multiresolution scheme is presented to deal with matrices with high percentages of missing data. Experimental results show the viability of the proposed approach.
In the multiple objects case, factorization techniques can not be directly applied to obtain the SFM of every object, since trajectories are not sorted into different objects. Furthermore, another problem should be faced out: the estimation of the rank of the matrix of trajectories. The problem is that, in this case, the rank of the matrix of trajectories is not bounded, since any prior knowledge about the number of objects nor about their motion is used. This problem becomes more difficult with missing data, since singular values can not be computed to estimate the rank. A technique to estimate the rank of a missing data matrix of trajectories is presented. The good performance of the proposed technique is empirically shown considering sequences with both, synthetic and real data. Once the rank is estimated and the matrix of trajectories is full, the motion segmentation of trajectories is computed. Finally, any factorization technique for the single object case gives the shape and motion of every object.
In addition to the SFM problem, this thesis also shows other applications that can be addressed by means of factorization techniques. Concretely, the Alternation technique, which is used through the thesis, is adapted to address each particular problem. The first proposed application is the photometric stereo: the goal is to recover the reflectance and surface normals and the light source direction at each frame, from a set of images taken under different lighting conditions. In a second application, the aim is to fill in missing data in gene expression matrices by using the Alternation technique. Finally, the Alternation technique is adapted to be applied in recommender systems, widely considered in E-commerce. For each application, experimental results are given in order to show the good performance of the proposed Alternation-based strategy.
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4

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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Анотація:
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 online fashion store, by running algorithms processing the data and using evaluation metrics to compare the results. We present results proving that for many types of implicit feedback data, matrix factorization techniques are inferior to various neighborhood- and association rules techniques for producing product recommendations. We also present a variant of a user-based neighborhood recommender system algorithm \textit{(UserNN)}, which in all tests we ran outperformed both the matrix factorization algorithms and the k-nearest neighbors algorithm regarding both accuracy and speed. Depending on what dataset was used, the UserNN achieved a precision approximately 2-22 percentage points higher than those of the matrix factorization algorithms, and 2 percentage points higher than the k-nearest neighbors algorithm. The UserNN also outperformed the other algorithms regarding speed, with time consumptions 3.5-5 less than those of the k-nearest neighbors algorithm, and several orders of magnitude less than those of the matrix factorization algorithms.
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5

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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Анотація:
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 donne des résultats plus interprétables dans le cas de sources binaires corrélées, et des rangs d'approximation matricielle plus faibles. Une condition nécessaire et suffisante d'unicité pour la FBMB est également fournie. Deux algorithmes s'appuyant sur une mise à jour multiplicative sont proposés et illustrés dans des simulations numériques ainsi que sur un jeu de données réelles. La généralisation de cette approche au cas de tableaux multidimensionnels (tenseurs) binaires conduit à la factorisation booléenne de tenseurs binaires (FBTB). La démonstration de la condition nécessaire et suffisante d’unicité de la décomposition booléenne de tenseurs binaires repose sur la notion d'indépendance booléenne d'une famille de vecteurs. L'algorithme multiplicatif fondé sur le modèle de mélange post non-linéaire est étendu au cas multidimensionnel. Nous proposons également un nouvel algorithme, plus efficace, s'appuyant sur une stratégie de type AO-ADMM (Alternating Optimization -ADMM). Ces algorithmes sont comparés à ceux de l'état de l'art sur des données simulées et sur un jeu de données réelles
This work is dedicated to the study of boolean decompositions of binary multidimensional arrays using a post nonlinear mixture model. In the first part, we introduce a new approach for the boolean factorization of binary matrices (BFBM) based on a post nonlinear mixture model. Unlike the existing binary matrix factorization methods, the proposed method is equivalent to the boolean factorization model when the matrices are strictly binary and give thus more interpretable results in the case of correlated sources and lower rank matrix approximations compared to other state-of-the-art algorithms. A necessary and suffi-cient condition for the uniqueness of the BFBM is also provided. Two algorithms based on multiplicative update rules are proposed and tested in numerical simulations, as well as on a real dataset. The gener-alization of this approach to the case of binary multidimensional arrays (tensors) leads to the boolean factorisation of binary tensors (BFBT). The proof of the necessary and sufficient condition for the boolean decomposition of binary tensors is based on a notion of boolean independence of binary vectors. The multiplicative algorithm based on the post nonlinear mixture model is extended to the multidimensional case. We also propose a new algorithm based on an AO-ADMM (Alternating Optimization-ADMM) strategy. These algorithms are compared to state-of-the-art algorithms on simulated and on real data
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6

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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Анотація:
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 application to the retail industry where companies may want to scour the web in order to find documents (blogs, Amazon reviews, etc.) which both speak about their product, and give an opinion on their product (positive, negative or neutral). My solution to this problem uses a Non-negative Matrix Factorization (NMF) technique in order to determine the topic classifications of a document set, and further factors the matrix in order to discover the sentiment behind this category of product.
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7

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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Анотація:
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 la quasi-périodicité, des signaux ECG et PCG ont été exploitées. Plusieurs propositions ont été mises en concurrence, basées sur la factorisation non-négative des matrices (NMF), une approche de décomposition matricielle adaptée aux signaux physiologiques.La solution finalement proposée pour l’estimation du RCF est basée sur une modélisation source-filtre des signaux ECG ou PCG fœtaux, préalablement extraits, permettant une estimation de la fréquence fondamentale par NMF.L'approche a été évaluée sur une base de données cliniques de signaux ECG et PCG sur femmes enceintes et les RCF obtenus ont été validés par comparaison à la technique clinique de référence par cardiotocographie
With more than 200,000 births per day in the world, fetal well-being monitoring during birth is a major clinical challenge. This monitoring is done by analyzing the fetal heart rate (FHR) and its variability, and this has to be robust while minimizing the number of non-invasive sensors to lay on the mother's abdomen.In this context, electrocardiogram (ECG) and phonocardiogram (PCG) signals are of interest since they both bring cardiac information, both redundant and complementary. This multimodality as well as some features of ECG and PCG signals, as quasi-periodicity, have been exploited. Several propositions were put in competition, based on non-negative matrix factorization (NMF), a matrix decomposition algorithm adapted to physiological signals.The final solution proposed for the FHR estimation is based on a source-filter modeling of real fetal ECG or PCG signals, previously extracted, allowing an estimation of the fundamental frequency by NMF.The approach was carried out on a clinical database of ECG and PCG signals on pregnant women and FHR results were validated by comparison with the cardiotocography clinical reference technique
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8

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 exploitant différentes connaissances a priori d'ordre spatial et temporel sur les sources. Les principales connaissances intégrées ici sont les régions d'intérêt (ROI) des sources qui apportent des informations spatiales riches. Contrairement aux travaux antérieurs qui ont une approche binaire, nous intégrons cette connaissance de manière robuste à la méthode de séparation, afin que cette dernière ne soit pas sensible aux variations inter et intra-utilisateurs dans la sélection des ROI. La méthode de séparation générique proposée prend la forme d'une fonctionnelle à minimiser, constituée d'un terme d'attache aux données ainsi que de pénalisations et de relâchements de contraintes exprimant les connaissances a priori. L'étude sur des images de synthèse montrent les bons résultats de notre approche par rapport à l'état de l'art. Deux applications, l'une sur les reins, l'autre sur le cœur illustrent les résultats sur des données cliniques réelles
In nuclear imaging (scintigraphy, SPECT, PET), diagnostics are often made with time activity curves (TAC) of organs and tissues. These TACs represent the dynamic evolution of tracer distribution inside patient's body. Extraction of TACs can be complicated by overlapping in the 2D image sequences, hence source separation methods must be used in order to extract TAC properly. However, the underlying separation problem is underdetermined. We propose to overcome this difficulty by adding some spatial and temporal prior knowledge about sources on the separation process. The main knowledge used in this work is region of interest (ROI) of organs and tissues. Unlike state of the art methods, ROI are integrated in a robust way in our method, in order to face user-dependancy in their selection. The proposed method is generic and minimize an objective function composed with a data fidelity criterion, penalizations and relaxations expressing prior knowledge. Results on synthetic datasets show the efficiency of the proposed method compare to state of the art methods. Two clinical applications on the kidney and on the heart are also adressed
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9

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 exacte pour la programmation en variables mixtes entières. Le deuxième chapitre est consacré au développement d'une méthode DCA pour la résolution d'une classe NP-difficile des programmes non convexes non linéaires en variables mixtes entières. Ces problèmes d'optimisation non convexe sont tout d'abord reformulées comme des programmes DC via les techniques de pénalisation en programmation DC de manière que les programmes DC résultants soient efficacement résolus par DCA et B&B bien adaptés. Comme première application en optimisation financière, nous avons modélisé le problème de gestion de portefeuille sous le coût de transaction concave et appliqué DCA et B&B à sa résolution. Dans le chapitre suivant nous étudions la modélisation du problème de minimisation du coût de transaction non convexe discontinu en gestion de portefeuille sous deux formes : la première est un programme DC obtenu en approximant la fonction objectif du problème original par une fonction DC polyèdrale et la deuxième est un programme DC mixte 0-1 équivalent. Et nous présentons DCA, B&B, et l'algorithme combiné DCA-B&B pour leur résolution. Le chapitre 4 étudie la résolution exacte du problème multi-objectif en variables mixtes binaires et présente deux applications concrètes de la méthode proposée. Nous nous intéressons dans le dernier chapitre à ces deux problématiques challenging : le problème de moindres carrés linéaires en variables entières bornées et celui de factorisation en matrices non négatives (Nonnegative Matrix Factorization (NMF)). La méthode NMF est particulièrement importante de par ses nombreuses et diverses applications tandis que les applications importantes du premier se trouvent en télécommunication. Les simulations numériques montrent la robustesse, rapidité (donc scalabilité), performance et la globalité de DCA par rapport aux méthodes existantes.
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10

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
國立臺灣大學
資訊工程學研究所
105
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 two-stage Randomized Response algorithm. The privacy of this framework is proved to be with the guarantee of differential privacy. We also conduct experiments on numerical feedback task and one-class feedback task. The results demonstrate that our framework can successfully achieve privacy with certain utility.
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Книги з теми "MATRIX FACTORIZATION TECHNIQUES"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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Частини книг з теми "MATRIX FACTORIZATION TECHNIQUES"

1

Symeonidis, Panagiotis, and Andreas Zioupos. "Related Work on Matrix Factorization." In Matrix and Tensor Factorization Techniques for Recommender Systems, 19–31. Cham: 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, 69–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_5.

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3

Balu, Raghavendran, Teddy Furon, and Laurent Amsaleg. "Sketching Techniques for Very Large Matrix Factorization." In Lecture Notes in Computer Science, 782–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30671-1_68.

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4

Symeonidis, Panagiotis, and Andreas Zioupos. "Experimental Evaluation on Matrix Decomposition Methods." In Matrix and Tensor Factorization Techniques for Recommender Systems, 59–65. Cham: 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, 3–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0_1.

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6

Symeonidis, Panagiotis, and Andreas Zioupos. "Performing SVD on Matrices and Its Extensions." In Matrix and Tensor Factorization Techniques for Recommender Systems, 33–57. Cham: 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, 81–93. Cham: 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, 95–99. Cham: 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, 101–2. Cham: 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, 204–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_20.

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Тези доповідей конференцій з теми "MATRIX FACTORIZATION TECHNIQUES"

1

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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2

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

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3

Baltrunas, Linas, Bernd Ludwig, and Francesco Ricci. "Matrix factorization techniques for context aware recommendation." In the fifth ACM conference. New York, New York, USA: 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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6

Siy, Peter W., Richard A. Moffitt, R. Mitchell Parry, Yanfeng Chen, Ying Liu, M. Cameron Sullards, Alfred H. Merrill, and May D. Wang. "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. New York, NY, USA: 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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Звіти організацій з теми "MATRIX FACTORIZATION TECHNIQUES"

1

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

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