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Auswahl der wissenschaftlichen Literatur zum Thema „Tensor PCA“
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Zeitschriftenartikel zum Thema "Tensor PCA"
Zare, Ali, Alp Ozdemir, Mark A. Iwen und Selin Aviyente. „Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA“. Proceedings of the IEEE 106, Nr. 8 (August 2018): 1341–58. http://dx.doi.org/10.1109/jproc.2018.2848209.
Der volle Inhalt der QuelleWang, An-Dong, Zhong Jin und Jing-Yu Yang. „A faster tensor robust PCA via tensor factorization“. International Journal of Machine Learning and Cybernetics 11, Nr. 12 (24.06.2020): 2771–91. http://dx.doi.org/10.1007/s13042-020-01150-2.
Der volle Inhalt der QuelleJagannath, Aukosh, Patrick Lopatto und Léo Miolane. „Statistical thresholds for tensor PCA“. Annals of Applied Probability 30, Nr. 4 (August 2020): 1910–33. http://dx.doi.org/10.1214/19-aap1547.
Der volle Inhalt der QuelleBen Arous, Gérard, Reza Gheissari und Aukosh Jagannath. „Algorithmic thresholds for tensor PCA“. Annals of Probability 48, Nr. 4 (Juli 2020): 2052–87. http://dx.doi.org/10.1214/19-aop1415.
Der volle Inhalt der QuelleJiang, Bo, Shiqian Ma und Shuzhong Zhang. „Low-M-Rank Tensor Completion and Robust Tensor PCA“. IEEE Journal of Selected Topics in Signal Processing 12, Nr. 6 (Dezember 2018): 1390–404. http://dx.doi.org/10.1109/jstsp.2018.2873144.
Der volle Inhalt der QuelleLiu, Cong, Xu Wei-sheng und Wu Qi-di. „Tensorial Kernel Principal Component Analysis for Action Recognition“. Mathematical Problems in Engineering 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/816836.
Der volle Inhalt der QuelleOuerfelli, Mohamed, Mohamed Tamaazousti und Vincent Rivasseau. „Random Tensor Theory for Tensor Decomposition“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 7 (28.06.2022): 7913–21. http://dx.doi.org/10.1609/aaai.v36i7.20761.
Der volle Inhalt der QuelleZhang, Hongjun, Peng Li, Weibei Fan, Zhuangzhuang Xue und Fanshuo Meng. „Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition“. Computational Intelligence and Neuroscience 2022 (06.09.2022): 1–12. http://dx.doi.org/10.1155/2022/7396185.
Der volle Inhalt der QuelleQiu, Yuning, Guoxu Zhou, Zhenhao Huang, Qibin Zhao und Shengli Xie. „Efficient Tensor Robust PCA Under Hybrid Model of Tucker and Tensor Train“. IEEE Signal Processing Letters 29 (2022): 627–31. http://dx.doi.org/10.1109/lsp.2022.3143721.
Der volle Inhalt der QuelleYang, Sihai, Xian-Hua Han und Yen-Wei Chen. „GND-PCA Method for Identification of Gene Functions Involved in Asymmetric Division of C. elegans“. Mathematics 11, Nr. 9 (25.04.2023): 2039. http://dx.doi.org/10.3390/math11092039.
Der volle Inhalt der QuelleDissertationen zum Thema "Tensor PCA"
Carletti, Davide. „Applicazioni dell'analisi tensoriale delle componenti principali“. Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Den vollen Inhalt der Quelle findenReising, Justin. „Function Space Tensor Decomposition and its Application in Sports Analytics“. Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3676.
Der volle Inhalt der QuellePiccolo, Vanessa. „Quelques problèmes de matrices aléatoires et de statistiques en grande dimension“. Electronic Thesis or Diss., Lyon, École normale supérieure, 2025. http://www.theses.fr/2025ENSL0002.
Der volle Inhalt der QuelleThis thesis explores some problems in random matrix theory and high-dimensional statistics motivated by the need to improve our understanding of deep learning. Training deep neural networks involves solving high-dimensional, large-scale, and nonconvex optimization problems that should, in theory, be intractable but are surprisingly feasible in practice. To understand this paradox, we study solvable models that balance practical relevance with rigorous mathematical analysis. Random matrices and high-dimensional statistics are central to these efforts due to the large datasets and high dimensionality inherent in such models. We first consider the random features model, a two-layer neural network with fixed random weights in the first layer and learnable weights in the second layer. Our focus is on the asymptotic spectrum of the conjugate kernel matrix YY* with Y = f(WX), where W and X are rectangular random matrices with i.i.d. entries and f is a nonlinear activation function applied entry-wise. We extend prior results on light-tailed distributions for W and X by considering two new settings. First, we study the case of additive bias Y = f(WX + B), where B is an independent rank-one Gaussian random matrix, closer modeling the neural network architectures encountered in practice. To obtain the asymptotics for the empirical spectral density we follow the resolvent method via the cumulant expansion. Second, we investigate the case where W has heavy-tailed entries, X remains light-tailed, and f is a smooth, bounded, and odd function. We show that heavy-tailed weights induce much stronger correlations among the entries of Y, resulting in a novel spectral behavior. This analysis relies on the moment method through traffic probability theory. Next, we address the tensor PCA (Principal Component Analysis) problem, a high-dimensional inference task that investigates the computational hardness of estimating an unknown signal vector from noisy tensor observations via maximum likelihood estimation. Tensor PCA serves as a prototypical framework for understanding high-dimensional nonconvex optimization through gradient-based methods. This understanding can be approached from two perspectives: the topological complexity of the optimization landscape and the training dynamics of first-order optimization methods. In the context of landscape complexity, we study the annealed complexity of random Gaussian homogeneous polynomials on the N-dimensional unit sphere in the presence of deterministic polynomials that depend on fixed unit vectors and external parameters. Using the Kac-Rice formula and determinant asymptotics for spiked Wigner matrices, we derive variational formulas for the exponential asymptotics of the average number critical points and local maxima. Concerning the optimization dynamics in high dimensions, we study stochastic gradient descent (SGD) and gradient flow (GF) for the multi-spiked tensor model, where the goal is to recover r orthogonal spikes from noisy tensor observations. We show that SGD achieves the same computational threshold than in the single-spike case. In contrast, GF requires more samples to recover all spikes, resulting in a suboptimal threshold compared to SGD. Our analysis shows that spikes are recovered through a "sequential elimination" process: once a correlation exceeds a critical threshold, competing correlations become sufficiently small, allowing the next correlation to grow and become macroscopic. The order of recovery depends on initial values of correlations and the corresponding signal-to-noise ratios (SNRs), leading to recovery of a permutation of the spikes. In the matrix case (p=2), sufficiently separated SNRs allow exact recovery of the spikes, while equal SNRs lead to recovery of the subspace spanned by the spikes
Vasconcelos, Francisco Herbert Lima. „AnÃlise do contexto e dos resultados da aprendizagem da avaliaÃÃo educacional em um curso de graduaÃÃo em Engenharia“. Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14573.
Der volle Inhalt der QuelleA avaliaÃÃo educacional dispÃe de mÃtodos para a obtenÃÃo de dados que podem ser Ãteis para avaliar grupos de indivÃduos (alunos, professores, administradores, tÃcnicos e outros), projetos, produtos e materiais, instituiÃÃes e sistemas educacionais, nos seus diversos nÃveis e competÃncias. No campo da educaÃÃo em engenharia, os processos avaliativos podem ajudar os gestores a tomarem decisÃes e a realizarem mudanÃas em cursos de graduaÃÃo. Esta tese investiga de forma inÃdita uma nova abordagem para a anÃlise e interpretaÃÃo de dados no campo da educaÃÃo em engenharia com Ãnfase no processo de avaliaÃÃo, levando em consideraÃÃo dois aspectos de modo integrado: a) a percepÃÃo/opiniÃo dos estudantes sobre o contexto/ambiente educacional (Learning Context - LC) e b) os resultados/rendimentos obtidos pelos mesmos discentes (Learning Outcomes - LO). Para a realizaÃÃo desta pesquisa, foram coletados dados de estudantes do curso de graduaÃÃo em Engenharia de TeleinformÃtica (ETI) do Centro de Tecnologia (CT) da Universidade Federal do Cearà (UFC). Os dados de LC foram coletados a partir da aplicaÃÃo do instrumento SEEQ (Studentâs Evaluation of Educational Quality) da metodologia SETE (Student Evaluate Teaching Effetivecness). Os dados de LO foram coletados a partir das informaÃÃes dos resultados de desempenho da aprendizagem dos mesmos discentes. Na realizaÃÃo do processamento da informaÃÃo dos dados matriciais e tensoriais obtidos, foram utilizadas duas ferramentas matemÃticas: a decomposiÃÃo bilinear, por meio da AnÃlise de Componentes Principais (Principal Component Analysis - PCA) e a decomposiÃÃo multilinear tensorial por meio da AnÃlise de Fatores Paralelos (Parallel Factor Analysis - PARAFAC). Os resultados obtidos permitem identificar caracterÃsticas comuns e semelhanÃas em componentes curriculares, tanto em termos da percepÃÃo quanto do desempenho dos estudantes. Os modelos PCA e PARAFAC tambÃm demonstraram um potencial significativo para extrair informaÃÃes de dados relacionados com variÃveis latentes em contextos educativos.
Educational evaluation provides methods to obtain data that can be useful for evaluating groups of individuals (students, teachers, administrators, technicians and others), projects, products and materials, educational institutions and systems at different levels and skills. In engineering education, evaluation processes can help managers to make decisions and changes in undergraduate courses. This thesis investigates in unprecedented way a new approach to the analysis and interpretation of data in the field of engineering education with emphasis in the evaluation process, taking into account two aspects in an integrated manner: a) perception / opinion of students about the context / educational environment (Learning Context - LC) and b) the results / income earned by the same students (Learning outcomes - LO). For this research, we collected data related to undergraduate students in Teleinformatics Engineering (TEI), at Technology Center (CT) of the Federal University of Cearà (UFC). LC data were collected from the application of SEEQ (Studentâs Evaluation of Educational Quality) instrument of SETE (Student Teaching Evaluate Effetivecness) methodology. The LO data was collected from the information of the performance of the studentsâ learning outcomes. Carrying out the information processing of the obtained tensor and matrix data, we have used two mathematical tools: the bilinear decomposition, called Principal Component Analysis - PCA decomposition and the multilinear tensor decomposition by Parallel Factor Analysis - PARAFAC. The results allow us to identify common features and similarities in curriculum components, both in terms of perception as the performance of students. The PCA and PARAFAC models also showed significant potential to extract data information related to latent variables in educational settings.
Swinarski, Marie. „PCP-driven cardiac remodeling couples changes in actomyosin tension with myocyte differentiation“. Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17775.
Der volle Inhalt der QuelleFormation of a complex multiple-chambered heart from the simple linear heart tube does not only require orchestrated morphogenesis of the myocardium, but also cardiac muscle differentiation and changes in intercellular electrical coupling. To date, the processes that lead to the formation of a functional syncytium are incompletely understood. One of the major pathways controlling multiple aspects of organogenesis and tissue morphogenesis is the planar cell polarity (PCP) pathway. Changes in tissue architecture are controlled by cell intercalation and collective cell migration. It is widely accepted that Wnt/PCP signaling plays a crucial role in guiding these cellular processes. This study provides evidence that morphogenesis of the heart is controlled by the non-canonical ligands Wnt11 and Wnt5b and the PCP core components Fzd7, Vangl2, Dvl2, and Pk1 through regulation of cell rearrangements during embryonic cardiac remodeling. Downstream effectors of the PCP pathway target adhesion processes, cytoskeleton, and migration. Here, it is revealed that PCP signaling in the heart affects cardiomyocyte morphology and actomyosin organization. Specifically, changes in the subcellular localization of the phosphorylated non-muscle myosin II regulatory light chain (pMRLC) at LHT stage are targeted by the PCP pathway core components. Furthermore, actomyosin relocalization concurs with changes in nuclear tension and SRF signal transduction within the myocardium. This study unravels a novel function of the PCP core component Pk1 in regulation of SRF translocation and target gene expression that is critical to cardiac maturation. Taken together, this study provides evidence that the PCP pathway is a major regulator of cardiac remodeling and organ maturation by modulating mechanosensitive SRF signal transduction involved in muscle differentiation.
Sylla, Diogone. „Fusion de données provenant de différents capteurs satellitaires pour le suivi de la qualité de l'eau en zones côtières. Application au littoral de la région PACA“. Thesis, Toulon, 2014. http://www.theses.fr/2014TOUL0013/document.
Der volle Inhalt der QuelleMonitoring coastal areas requires both a good spatial resolution, good spectral resolution associated with agood signal to noise ratio and finally a good temporal resolution to visualize rapid changes in water color.Available now, and even those planed soon, sensors do not provide both a good spatial, spectral ANDtemporal resolution. In this study, we are interested in the image fusion of two future sensors which are bothpart of the Copernicus program of the European Space Agency: MSI on Sentinel-2 and OLCI on Sentinel-3.Such as MSI and OLCI do not provide image yet, it was necessary to simulate them. We then used thehyperspectral imager HICO and we then proposed three methods: an adaptation of the method ARSIS fusionof multispectral images (ARSIS), a fusion method based on the non-negative factorization tensors (Tensor)and a fusion method based on the inversion de matrices (Inversion).These three methods were first evaluated using statistical parameters between images obtained by fusionand the "perfect" image as well as the estimation results of biophysical parameters obtained by minimizingthe radiative transfer model in water
Swinarski, Marie [Verfasser], Holger Gutachter] Gerhardt, Christian [Gutachter] Mosimann und Thomas [Gutachter] [Sommer. „PCP-driven cardiac remodeling couples changes in actomyosin tension with myocyte differentiation / Marie Swinarski ; Gutachter: Holger Gerhardt, Christian Mosimann, Thomas Sommer“. Berlin : Lebenswissenschaftliche Fakultät, 2017. http://d-nb.info/1135241562/34.
Der volle Inhalt der QuelleSwinarski, Marie Verfasser], Holger [Gutachter] Gerhardt, Christian [Gutachter] Mosimann und Thomas [Gutachter] [Sommer. „PCP-driven cardiac remodeling couples changes in actomyosin tension with myocyte differentiation / Marie Swinarski ; Gutachter: Holger Gerhardt, Christian Mosimann, Thomas Sommer“. Berlin : Lebenswissenschaftliche Fakultät, 2017. http://d-nb.info/1135241562/34.
Der volle Inhalt der QuelleNehme, Nada. „Évaluation de la qualité de l’eau du bassin inférieur de la rivière du Litani, Liban : approche environnementale“. Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0296/document.
Der volle Inhalt der QuelleThe objective of this study was to evaluate water quality of the lower Litani River and assess its feasibility for multi-purpose usage as one of the solutions to the aggravated water problems in Lebanon, To identify possible sources of metals (i.e. geological and/or anthropogenic) and then to characterize the chemical behavior of these metals in water and bed sediments, water and bed load sediments were sampled at six representatives sites which are investigated over three seasons of the year 2011-2012 (i.e. rainy, mid-rainy and dry seasons), The PCA (principal component analysis) method was used to interpret the elemental concentrations in the river water. Results show that among 18 variables, which were evaluated to characterize their physic-chemistry and metals, there are only 4 (Fe, NO2-, CaCO3, Cu) that were determined the type of environmental studied; Three groups were identified and differentiated by PCA according to the seasons. The first group includes all statements made in the mid rainy season and has a dialogue rich in Fe and NO2, and low in NH4 and EC. The second group formed in dry season, and surveys show physicochemical characteristics opposite to those in the first group, the third group formed in mid rainy, and showed the low concentration of K+, PO43- and Cl-. Sediments were characterized by a set of cations exchange capacity, granulometric, diffraction (XRD) and Fourier transformed infrared spectroscopy (FTIR( The FTIR analysis shows that the amount of montomorillonite is less than kaolinite and very much less than quartz and calcite. Pearson’s correlation was also performed in this study to compare to and determine the correlation between heavy metals in the sediments. Geo-accumulation (Igeo) index, Contamination Factor (Cf), and contamination degree (Cd) were also applying to assess the level of contamination in the sites. The results shows that the concentration of Pb, Fe are high in the site S5, S6 and the value of Cr ,Ni, are high in the S6, this results suggest that special attention must be given to the issue of element re-mobilization, because a large portion of elements in sediments are likely to release back into the water column. All the sites are characterized by moderate to highly microbial polluted range. The degree of contamination was increased in dry season. However, no critical physicochemical pollution has been reported in this part of the river; except the high concentration of Fe and NO2- in all investigated sites due to the reject of wastewater and to the distribution of touristic activities in the LLRB, the Concentrations of seven heavy metals is high(Cu, Fe, Cd , Mn , Cr, Zn, Ni and Pb) for sediment is higher according to Consensus-based sediment quality guidelines of Wisconsin (Wisconsin Department of Natural Resources, 2003) were applied to assess metal contamination in sediment
Chis, Mihaela-Ana. „Mesure du tenseur de susceptibilité non linéaire d'ordre trois par traitement d'images“. Angers, 1996. http://www.theses.fr/1996ANGE0005.
Der volle Inhalt der QuelleBücher zum Thema "Tensor PCA"
Scottoline, Lisa. Legal tender. New York: HarperCollins Publishers, 1996.
Den vollen Inhalt der Quelle findenScottoline, Lisa. Legal tender. New York: HarperPaperbacks, 1997.
Den vollen Inhalt der Quelle findenScottoline, Lisa. Legal Tender. New York: HarperCollins, 2002.
Den vollen Inhalt der Quelle findenScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 1998.
Den vollen Inhalt der Quelle findenScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 2008.
Den vollen Inhalt der Quelle findenScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 2009.
Den vollen Inhalt der Quelle findenMaggiore, Michele. Gravitational Waves. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.001.0001.
Der volle Inhalt der QuelleScottoline, Lisa. Legal Tender: A Rosato and Associates Novel. HarperCollins Publishers, 2016.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Tensor PCA"
Houthuys, Lynn, und Johan A. K. Suykens. „Tensor Learning in Multi-view Kernel PCA“. In Artificial Neural Networks and Machine Learning – ICANN 2018, 205–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01421-6_21.
Der volle Inhalt der QuelleWang, Andong, Zhong Jin und Jingyu Yang. „A Factorization Strategy for Tensor Robust PCA“. In Lecture Notes in Computer Science, 424–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41404-7_30.
Der volle Inhalt der QuelleInoue, Kohei. „Generalized Tensor PCA and Its Applications to Image Analysis“. In Applied Matrix and Tensor Variate Data Analysis, 51–71. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55387-8_3.
Der volle Inhalt der QuelleShalaby, Ahmed, Aly Farag und Melih Aslan. „2D-PCA Based Tensor Level Set Framework for Vertebral Body Segmentation“. In Lecture Notes in Computational Vision and Biomechanics, 35–48. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07269-2_4.
Der volle Inhalt der QuelleVenkatachalam, K., Nebojsa Bacanin, Enamul Kabir und P. Prabu. „Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP – EffUnet Classification“. In Health Information Science, 181–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90885-0_17.
Der volle Inhalt der QuelleHan, Xian-Hua, Yen-Wei Chen und Xiang Ruan. „Multilinear Tensor Supervised Neighborhood Embedding Analysis for View-Based Object Recognition“. In Advances in Multimedia Information Processing - PCM 2010, 236–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15702-8_22.
Der volle Inhalt der QuelleWang, Jian, Xian-Hua Han, Jiande Sun, Lanfen Lin, Hongjie Hu, Yingying Xu, Qingqing Chen und Yen-Wei Chen. „Focal Liver Lesion Classification Based on Tensor Sparse Representations of Multi-phase CT Images“. In Advances in Multimedia Information Processing – PCM 2018, 696–704. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00767-6_64.
Der volle Inhalt der Quelle„6 Tender adjudication“. In Project cost estimating, 60–68. Thomas Telford Publishing, 1995. http://dx.doi.org/10.1680/pce.20320.0006.
Der volle Inhalt der QuelleParamasivam, Karthika, Prathap M. und Hussain Sharif. „Heterogeneous Large-Scale Distributed Systems on Machine Learning“. In Deep Neural Networks for Multimodal Imaging and Biomedical Applications, 47–68. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3591-2.ch004.
Der volle Inhalt der QuelleBiondi, Karina. „Conclusion“. In Sharing This Walk, herausgegeben von John F. Collins. University of North Carolina Press, 2016. http://dx.doi.org/10.5149/northcarolina/9781469623405.003.0006.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Tensor PCA"
BENKENDORFF, NAYARA JULIANA JARGEMBOSKI PIAZERA, Breno Salgado Barra, NATAN ASSIS MONTEIRO und RAFAEL CRISTYAN FRONZA. „AVALIAÇÃO DE MISTURAS ASFÁLTICAS DENSAS COM USO DE ESCÓRIA DE BATERIA DE CHUMBO-ÁCIDO“. In Anais da 49ª Reunião Anual de Pavimentação - RAPv, 567–78. Recife, Brasil: Even3, 2024. http://dx.doi.org/10.29327/1430212.49-51.
Der volle Inhalt der QuelleZhou, Pan, und Jiashi Feng. „Outlier-Robust Tensor PCA“. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.419.
Der volle Inhalt der QuelleShahid, Nauman, Francesco Grassi und Pierre Vandergheynst. „Tensor Robust PCA on Graphs“. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682990.
Der volle Inhalt der QuelleGoyal, Navin, Santosh Vempala und Ying Xiao. „Fourier PCA and robust tensor decomposition“. In STOC '14: Symposium on Theory of Computing. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2591796.2591875.
Der volle Inhalt der QuelleRen, Jineng, Xingguo Li und Jarvis Haupt. „Robust PCA via tensor outlier pursuit“. In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869681.
Der volle Inhalt der QuelleWein, Alexander S., Ahmed El Alaoui und Cristopher Moore. „The Kikuchi Hierarchy and Tensor PCA“. In 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2019. http://dx.doi.org/10.1109/focs.2019.000-2.
Der volle Inhalt der QuelleTountas, Konstantinos, Dimitris G. Chachlakis, Panos P. Markopoulos und Dimitris A. Pados. „Iteratively Re-weighted L1-PCA of Tensor Data“. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048775.
Der volle Inhalt der QuelleLu, Canyi. „Transforms based Tensor Robust PCA: Corrupted Low-Rank Tensors Recovery via Convex Optimization“. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00118.
Der volle Inhalt der QuelleZhang, Sheng-Nan, Yu-Lin Zhang, Jin-Xing Liu, Juan Wang, Junliang Shang und Dao-Hui Ge. „Tensor Robust PCA Based on Transformed Tensor Singular Value Decomposition for Cancer Genomic Data“. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994952.
Der volle Inhalt der QuelleDong, Harry, Megna Shah, Sean Donegan und Yuejie Chi. „Deep Unfolded Tensor Robust PCA With Self-Supervised Learning“. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095485.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Tensor PCA"
บุรณเวช, ศุภสวัสดิ์. การตรวจพิสูจน์และศึกษาเปรียบเทียบความหลากหลายทางพันธุกรรมของเชื้อไวรัส Torque Teno Virus (TTV) ในสุกรประเทศไทย : รายงานวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2013. https://doi.org/10.58837/chula.res.2013.89.
Der volle Inhalt der QuelleSandoval Rincón, Diana Marcela. Accesibilidad a servicios de agua y saneamiento, energía y transporte para personas con discapacidad en América Latina y el Caribe. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003613.
Der volle Inhalt der QuelleFranco Calderón, Ángela María, Gynna Millan Franco, Andrés Sepúlveda und Isabella Jaramillo Díaz. Policy Brief No. 5. Iniciativas comunitarias para el diseño y activación de espacios públicos en barrios populares. Universidad del Valle, März 2023. http://dx.doi.org/10.25100/policy-briefs.pb.05-esp.
Der volle Inhalt der QuelleBeverinotti, Javier, Luis Fernando Corrales, Tatiana Vargas und Jorge Chang. Diagnóstico de crecimiento de Costa Rica. Inter-American Development Bank, Juni 2014. http://dx.doi.org/10.18235/0009574.
Der volle Inhalt der QuelleLovera Viloria, Victor Alfonzo, und Sandra Sinde Cantorna. Smart Doctor: pandemia de COVID-19 y compra de innovación en salud: fortalecimiento de las capacidades de los países de la región para la implementación de metodologías de compra pública de innovación. Inter-American Development Bank, Februar 2023. http://dx.doi.org/10.18235/0004704.
Der volle Inhalt der QuelleAyala-García, Jhorland, Jaime Alfredo Bonet-Morón und María Beatriz García-Dereix. Museo de Arte Moderno de Cartagena (MAMC), una colección con 63 años de historia. Banco de la República, Juli 2023. http://dx.doi.org/10.32468/chee.59.
Der volle Inhalt der QuelleAraujo, G. A., T. A. Quintero, Andrés Miguel Quintero Gutiérrez und Medardo José Rodríguez Polo. Medición de la condición corporal del ganado Cebú. Universidad Nacional Abierta y a Distancia, Dezember 2020. http://dx.doi.org/10.22490/ecapma.3672.
Der volle Inhalt der QuelleWalker, Michael, Gill Holcombe, Clare Mills, Chiara Nitride und Adrian Rogers. Development of Reference Materials for food allergen analysis. Food Standards Agency, Juni 2023. http://dx.doi.org/10.46756/sci.fsa.hwt621.
Der volle Inhalt der QuelleBleichschmidt, Andreas. "Mobilität ist Kultur"? : die Beteiligung der Bevölkerung an der Entwicklung der Mobilitätskultur in Zürich und Frankfurt am Main im Vergleich. Goethe-Universität, Institut für Humangeographie, November 2012. http://dx.doi.org/10.21248/gups.27460.
Der volle Inhalt der QuelleAvilez Bedoya, Yeimi Marcela, Francisco José Montealegre Torres und Danilo Bonilla Trujillo. Desarrollo de un cultivo de cilantro (Coriandrum sativum) en un sistema aeropónico automatizado. Sello Editorial UNAD, November 2024. http://dx.doi.org/10.22490/ecapma.7480.
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