Literatura científica selecionada sobre o tema "Tensor PCA"
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Artigos de revistas sobre o assunto "Tensor PCA"
Zare, Ali, Alp Ozdemir, Mark A. Iwen e Selin Aviyente. "Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA". Proceedings of the IEEE 106, n.º 8 (agosto de 2018): 1341–58. http://dx.doi.org/10.1109/jproc.2018.2848209.
Texto completo da fonteWang, An-Dong, Zhong Jin e Jing-Yu Yang. "A faster tensor robust PCA via tensor factorization". International Journal of Machine Learning and Cybernetics 11, n.º 12 (24 de junho de 2020): 2771–91. http://dx.doi.org/10.1007/s13042-020-01150-2.
Texto completo da fonteJagannath, Aukosh, Patrick Lopatto e Léo Miolane. "Statistical thresholds for tensor PCA". Annals of Applied Probability 30, n.º 4 (agosto de 2020): 1910–33. http://dx.doi.org/10.1214/19-aap1547.
Texto completo da fonteBen Arous, Gérard, Reza Gheissari e Aukosh Jagannath. "Algorithmic thresholds for tensor PCA". Annals of Probability 48, n.º 4 (julho de 2020): 2052–87. http://dx.doi.org/10.1214/19-aop1415.
Texto completo da fonteJiang, Bo, Shiqian Ma e Shuzhong Zhang. "Low-M-Rank Tensor Completion and Robust Tensor PCA". IEEE Journal of Selected Topics in Signal Processing 12, n.º 6 (dezembro de 2018): 1390–404. http://dx.doi.org/10.1109/jstsp.2018.2873144.
Texto completo da fonteLiu, Cong, Xu Wei-sheng e 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.
Texto completo da fonteOuerfelli, Mohamed, Mohamed Tamaazousti e Vincent Rivasseau. "Random Tensor Theory for Tensor Decomposition". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 7913–21. http://dx.doi.org/10.1609/aaai.v36i7.20761.
Texto completo da fonteZhang, Hongjun, Peng Li, Weibei Fan, Zhuangzhuang Xue e Fanshuo Meng. "Tensor Multi-Clustering Parallel Intelligent Computing Method Based on Tensor Chain Decomposition". Computational Intelligence and Neuroscience 2022 (6 de setembro de 2022): 1–12. http://dx.doi.org/10.1155/2022/7396185.
Texto completo da fonteQiu, Yuning, Guoxu Zhou, Zhenhao Huang, Qibin Zhao e 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.
Texto completo da fonteYang, Sihai, Xian-Hua Han e Yen-Wei Chen. "GND-PCA Method for Identification of Gene Functions Involved in Asymmetric Division of C. elegans". Mathematics 11, n.º 9 (25 de abril de 2023): 2039. http://dx.doi.org/10.3390/math11092039.
Texto completo da fonteTeses / dissertações sobre o assunto "Tensor PCA"
Carletti, Davide. "Applicazioni dell'analisi tensoriale delle componenti principali". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Encontre o texto completo da fonteReising, Justin. "Function Space Tensor Decomposition and its Application in Sports Analytics". Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3676.
Texto completo da fontePiccolo, 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.
Texto completo da fonteThis 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.
Texto completo da fonteA 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.
Texto completo da fonteFormation 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.
Texto completo da fonteMonitoring 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 e 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.
Texto completo da fonteSwinarski, Marie Verfasser], Holger [Gutachter] Gerhardt, Christian [Gutachter] Mosimann e 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.
Texto completo da fonteNehme, 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.
Texto completo da fonteThe 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.
Texto completo da fonteLivros sobre o assunto "Tensor PCA"
Scottoline, Lisa. Legal tender. New York: HarperCollins Publishers, 1996.
Encontre o texto completo da fonteScottoline, Lisa. Legal tender. New York: HarperPaperbacks, 1997.
Encontre o texto completo da fonteScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 1998.
Encontre o texto completo da fonteScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 2008.
Encontre o texto completo da fonteScottoline, Lisa. Legal Tender. HarperCollins Publishers Limited, 2009.
Encontre o texto completo da fonteMaggiore, Michele. Gravitational Waves. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.001.0001.
Texto completo da fonteScottoline, Lisa. Legal Tender: A Rosato and Associates Novel. HarperCollins Publishers, 2016.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Tensor PCA"
Houthuys, Lynn, e 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.
Texto completo da fonteWang, Andong, Zhong Jin e 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.
Texto completo da fonteInoue, 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.
Texto completo da fonteShalaby, Ahmed, Aly Farag e 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.
Texto completo da fonteVenkatachalam, K., Nebojsa Bacanin, Enamul Kabir e 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.
Texto completo da fonteHan, Xian-Hua, Yen-Wei Chen e 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.
Texto completo da fonteWang, Jian, Xian-Hua Han, Jiande Sun, Lanfen Lin, Hongjie Hu, Yingying Xu, Qingqing Chen e 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.
Texto completo da fonte"6 Tender adjudication". In Project cost estimating, 60–68. Thomas Telford Publishing, 1995. http://dx.doi.org/10.1680/pce.20320.0006.
Texto completo da fonteParamasivam, Karthika, Prathap M. e 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.
Texto completo da fonteBiondi, Karina. "Conclusion". In Sharing This Walk, editado por John F. Collins. University of North Carolina Press, 2016. http://dx.doi.org/10.5149/northcarolina/9781469623405.003.0006.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Tensor PCA"
BENKENDORFF, NAYARA JULIANA JARGEMBOSKI PIAZERA, Breno Salgado Barra, NATAN ASSIS MONTEIRO e 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.
Texto completo da fonteZhou, Pan, e 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.
Texto completo da fonteShahid, Nauman, Francesco Grassi e 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.
Texto completo da fonteGoyal, Navin, Santosh Vempala e 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.
Texto completo da fonteRen, Jineng, Xingguo Li e 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.
Texto completo da fonteWein, Alexander S., Ahmed El Alaoui e 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.
Texto completo da fonteTountas, Konstantinos, Dimitris G. Chachlakis, Panos P. Markopoulos e 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.
Texto completo da fonteLu, 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.
Texto completo da fonteZhang, Sheng-Nan, Yu-Lin Zhang, Jin-Xing Liu, Juan Wang, Junliang Shang e 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.
Texto completo da fonteDong, Harry, Megna Shah, Sean Donegan e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Tensor PCA"
บุรณเวช, ศุภสวัสดิ์. การตรวจพิสูจน์และศึกษาเปรียบเทียบความหลากหลายทางพันธุกรรมของเชื้อไวรัส Torque Teno Virus (TTV) ในสุกรประเทศไทย : รายงานวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2013. https://doi.org/10.58837/chula.res.2013.89.
Texto completo da fonteSandoval 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, setembro de 2021. http://dx.doi.org/10.18235/0003613.
Texto completo da fonteFranco Calderón, Ángela María, Gynna Millan Franco, Andrés Sepúlveda e Isabella Jaramillo Díaz. Policy Brief No. 5. Iniciativas comunitarias para el diseño y activación de espacios públicos en barrios populares. Universidad del Valle, março de 2023. http://dx.doi.org/10.25100/policy-briefs.pb.05-esp.
Texto completo da fonteBeverinotti, Javier, Luis Fernando Corrales, Tatiana Vargas e Jorge Chang. Diagnóstico de crecimiento de Costa Rica. Inter-American Development Bank, junho de 2014. http://dx.doi.org/10.18235/0009574.
Texto completo da fonteLovera Viloria, Victor Alfonzo, e 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, fevereiro de 2023. http://dx.doi.org/10.18235/0004704.
Texto completo da fonteAyala-García, Jhorland, Jaime Alfredo Bonet-Morón e María Beatriz García-Dereix. Museo de Arte Moderno de Cartagena (MAMC), una colección con 63 años de historia. Banco de la República, julho de 2023. http://dx.doi.org/10.32468/chee.59.
Texto completo da fonteAraujo, G. A., T. A. Quintero, Andrés Miguel Quintero Gutiérrez e Medardo José Rodríguez Polo. Medición de la condición corporal del ganado Cebú. Universidad Nacional Abierta y a Distancia, dezembro de 2020. http://dx.doi.org/10.22490/ecapma.3672.
Texto completo da fonteWalker, Michael, Gill Holcombe, Clare Mills, Chiara Nitride e Adrian Rogers. Development of Reference Materials for food allergen analysis. Food Standards Agency, junho de 2023. http://dx.doi.org/10.46756/sci.fsa.hwt621.
Texto completo da fonteBleichschmidt, 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, novembro de 2012. http://dx.doi.org/10.21248/gups.27460.
Texto completo da fonteAvilez Bedoya, Yeimi Marcela, Francisco José Montealegre Torres e Danilo Bonilla Trujillo. Desarrollo de un cultivo de cilantro (Coriandrum sativum) en un sistema aeropónico automatizado. Sello Editorial UNAD, novembro de 2024. http://dx.doi.org/10.22490/ecapma.7480.
Texto completo da fonte