Search Results - machinery learning matrix factorization algorithm*

A Generalized Deep Learning Clustering Algorithm Based on Non-Negative Matrix Factorization

Authors: Dexian Wang Tianrui Li Ping Deng et al.

Source: ACM Transactions on Knowledge Discovery from Data. 17:1-20

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Self-supervised Bipartite Graph Representation Learning: A Dirichlet Max-margin Matrix Factorization Approach

Authors: Shenghai Zhong Shu Guo Jing Liu et al.

Source: ACM Transactions on Intelligent Systems and Technology. 15:1-24

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences

Discrete ranking-based matrix factorization with self-paced learnings

Authors: Zhang, Y Tsang, IW Wang, H et al.

Index Terms: Conference Proceeding

URL: http://hdl.handle.net/10453/149986
http://purl.org/au-research/grants/arc/FT130100746
http://purl.org/au-research/grants/arc/LP150100671
http://purl.org/au-research/grants/arc/DP180100106
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
10.1145/3219819.3220116
http://purl.org/au-research/grants/arc/FT130100746
http://purl.org/au-research/grants/arc/LP150100671
http://purl.org/au-research/grants/arc/DP180100106

Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery.

Authors: Soper B Lisicki M Silva M et al.

Source: Scientific reports [Sci Rep] 2025 Aug 25; Vol. 15 (1), pp. 31196. Date of Electronic Publication: 2025 Aug 25.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

MeSH Terms: HIV-1*/physiology , HIV-1*/genetics , HIV Infections*/genetics , HIV Infections*/virology , Epistasis, Genetic*, Bayes Theorem ; Humans ; Viral Load/genetics ; Algorithms ; Computational Biology/methods ; Gene Knockdown Techniques ; Computer Simulation

Privacy-Preserving Non-Negative Matrix Factorization with Outliers

Authors: Swapnil Saha Hafiz Imtiaz

Source: ACM Transactions on Knowledge Discovery from Data. 18:1-26

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2211.01451

Biclustering and boolean matrix factorization in data streams

Authors: Stefan Neumann Pauli Miettinen

Source: Proceedings of the VLDB Endowment. 13:1709-1722

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), 102019 Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, Data Structures and Algorithms (cs.DS), 02 engineering and technology, 102019 Machine learning, Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/2012.03138
http://arxiv.org/abs/2012.03138
https://ucrisportal.univie.ac.at/de/publications/b5c9870b-243e-4201-9e00-1e754276fd99
https://www.vldb.org/pvldb/vol13/p1709-neumann.pdf
https://doi.org/10.14778/3401960.3401968
https://arxiv.org/abs/2012.03138
https://dblp.uni-trier.de/db/journals/corr/corr2012.html#abs-2012-03138
https://arxiv.org/pdf/2012.03138
https://dl.acm.org/doi/10.14778/3401960.3401968

Research on CatBoost model based on AutoEncoder dimensionality reduction in pollution source apportionment.

Authors: Zhang S Ding W

Source: Environmental geochemistry and health [Environ Geochem Health] 2025 Nov 02; Vol. 47 (12), pp. 543. Date of Electronic Publication: 2025 Nov 02.

Publication Type: Journal Article

Journal Info: Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 8903118 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2983 (Electronic) Linking ISSN: 02694042 NLM ISO Abbreviation: Environ Geochem Health Subsets: MEDLINE

MeSH Terms: Environmental Monitoring*/methods , Models, Theoretical* , Water Pollution*/analysis , Water Pollutants, Chemical*/analysis, Rivers/chemistry ; China ; Dimensionality Reduction ; Autoencoder ; Boosting Machine Learning Algorithms

Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.

Authors: Su X Hu P Li D et al.

Source: Nature biomedical engineering [Nat Biomed Eng] 2025 Mar; Vol. 9 (3), pp. 371-389. Date of Electronic Publication: 2025 Jan 09.

Publication Type: Journal Article

Journal Info: Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE

MeSH Terms: Neoplasms*/genetics , Gene Regulatory Networks* , Computational Biology*/methods , Machine Learning* , Genes, Neoplasm*, Humans ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Algorithms ; Gene Expression Regulation, Neoplastic ; Databases, Genetic ; Transcription Factors/metabolism

Continual Multiview Task Learning via Deep Matrix Factorization.

Authors: Sun, Gan Cong, Yang Zhang, Yulun et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Jan2021, Vol. 32 Issue 1, p139-150, 12p

Subject Terms: MATRIX decomposition, DEEP learning, CONTINUING education, TASKS

Delighting Palates with AI: Reinforcement Learning's Triumph in Crafting Personalized Meal Plans with High User Acceptance.

Authors: Amiri, Maryam Sarani Rad, Fatemeh Li, Juan

Source: Nutrients. Feb2024, Vol. 16 Issue 3, p346. 17p.

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection.

Authors: Guven, Emine

Source: JMIR Bioinformatics & Biotechnology; 2023, Vol. 4 Issue 1, p1-11, 11p

Subject Terms: GENE expression, DNA microarrays, ACUTE myeloid leukemia, ALGORITHMS, COMPUTATIONAL biology

Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization.

Authors: Zhao, Xueyi Zhang, Zhongfei Li, Xi et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Dec2016, Vol. 27 Issue 12, p2628-2642, 15p

Subject Terms: NONNEGATIVE matrices, ONLINE algorithms, MATRIX decomposition

Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis.

Authors: Aria M Javanmard Z Pishdad D et al.

Source: Journal of evidence-based medicine [J Evid Based Med] 2025 Mar; Vol. 18 (1), pp. e70005.

Publication Type: Journal Article; Meta-Analysis; Systematic Review

Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101497477 Publication Model: Print Cited Medium: Internet ISSN: 1756-5391 (Electronic) Linking ISSN: 17565391 NLM ISO Abbreviation: J Evid Based Med Subsets: MEDLINE

MeSH Terms: Artificial Intelligence* , Leukemia*/diagnosis , Leukemia*/classification , Machine Learning*, Humans ; Algorithms ; Intelligent Systems

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Authors: Wall BPG Nguyen M Harrell JC et al.

Source: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2025; Vol. 2856, pp. 357-400.

Publication Type: Journal Article; Review

Journal Info: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE

MeSH Terms: Chromatin*/genetics , Chromatin*/metabolism , Deep Learning*, Humans ; Computational Biology/methods ; Machine Learning ; Genomics/methods ; Enhancer Elements, Genetic ; Promoter Regions, Genetic ; Binding Sites ; Genome ; Software

BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

Authors: Checco A. Bianchi G. Leith D. J. et al.

Contributors: Checco A. Bianchi G. Leith D. J. et al.

Source: ACM Transactions on Privacy and Security. 20:1-25

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Matrix factorization, Machine Learning (stat.ML), 02 engineering and technology, 01 natural sciences, Clustering, Machine Learning (cs.LG), Privacy, Statistics - Machine Learning, Recommender systems, 0202 electrical engineering, electronic engineering, information engineering, Settore ING-INF/03 - TELECOMUNICAZIONI, 0101 mathematics

File Description: application/pdf

Access URL: http://eprints.whiterose.ac.uk/129377/1/1509.05789.pdf
http://arxiv.org/abs/1509.05789
https://eprints.whiterose.ac.uk/129377/1/1509.05789.pdf
https://dblp.uni-trier.de/db/journals/corr/corr1509.html#CheccoBL15
https://ui.adsabs.harvard.edu/abs/2015arXiv150905789C/abstract
https://dl.acm.org/doi/10.1145/3041760
https://doi.org/10.1145/3041760
https://eprints.whiterose.ac.uk/129377/
https://hdl.handle.net/2108/240076
https://doi.org/10.1145/3041760
https://hdl.handle.net/11573/1680040
https://doi.org/10.1145/3041760

Decentralized Federated Recommendation with Privacy-aware Structured Client-level Graph

Authors: Zhitao Li Zhaohao Lin Feng Liang et al.

Source: ACM Transactions on Intelligent Systems and Technology. 15:1-23

Subject Terms: Graph machine learning, Matrix factorization, 0202 electrical engineering, electronic engineering, information engineering, Explicit feedback, 02 engineering and technology, Federated recommendation

Desarrollo de un sistema de detección multifalla para rodamientos ; Development of a Multi-Fault Detection System for Bearings

Authors: Alario Echavarría, Eduardo Javier Maradey Lázaro, Jessica Gissella Maradey Lázaro, Jessica Gissella 0000040553 et al.

Contributors: Alario Echavarría, Eduardo Javier Maradey Lázaro, Jessica Gissella Maradey Lázaro, Jessica Gissella 0000040553 et al.

Subject Terms: Mechatronic, Bearing fault detection, Vibration analysis, Cage wear detection, Neural networks, Machine learning, Artificial intelligence, Rotating machinery, Rolling element bearings, Mechanical failure analysis, Theory of machines, Computational algorithms, Automation, Technological innovations, Intelligent agents (Computer software), Bearings (Machinery), Mecatrónica, Teoría de las máquinas, Algoritmos computacionales, Automatización, Innovaciones tecnológicas, Agentes inteligentes (Software para computadores), Rodamientos (Maquinaria), Detección de fallas en rodamientos, Análisis de vibraciones, Detección de desgaste en la jaula, Redes neuronales, Aprendizaje automático, Inteligencia artificial, Maquinaria rotativa

Subject Geographic: Colombia, UNAB Campus Bucaramanga

File Description: application/pdf

Relation: H. Gao, L. Liang, X. Chen, y G. Xu, «Feature extraction and recognition for rolling element bearing fault utilizing short-time fourier transform and non-negative matrix factorization», Chinese Journal of Mechanical Engineering (English Edition), vol. 28, n.o 1, pp. 96-105, ene. 2015, doi:10.3901/CJME.2014.1103.166.; O. Janssens et al., «Convolutional Neural Network Based Fault Detection for Rotating Machinery», J Sound Vib, vol. 377, pp. 331-345, sep. 2016, doi:10.1016/j.jsv.2016.05.027.; Y. Xie y T. Zhang, «Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition», Shock and Vibration, vol. 2017, 2017, doi:10.1155/2017/3084197.; N. Upadhyay y P. K. Kankar, «Diagnosis of bearing defects using tunable Q-wavelet transform», Journal of Mechanical Science and Technology, vol. 32, n.o 2, pp. 549-558, feb. 2018, doi:10.1007/s12206-018-0102-8.; C. Malla y I. Panigrahi, «Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques», Journal of Vibration Engineering and Technologies, vol. 7, n.o 4, pp. 407-414, ago. 2019, doi:10.1007/s42417-019-00119-y.; Z. Chen, A. Mauricio, W. Li, y K. Gryllias, «A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks», Mech Syst Signal Process, vol. 140, jun. 2020, doi:10.1016/j.ymssp.2020.106683.; C. T. Alexakos, Y. L. Karnavas, M. Drakaki, y I. A. Tziafettas, «A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors», Mach Learn Knowl Extr, vol. 3, n.o 1, pp. 228-242, mar. 2021, doi:10.3390/make3010011.; S. M. Tayyab, S. Chatterton, y P. Pennacchi, «Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps», Sensors, vol. 22, n.o 5, mar. 2022, doi:10.3390/s22052026.; L. C. Brito, G. A. Susto, J. N. Brito, y M. A. V. Duarte, «Fault Diagnosis using eXplainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data», Expert Syst Appl, vol. 232, dic. 2023, doi:10.1016/j.eswa.2023.120860.; J. G. M. Lázaro, C. A. F. González, y G. A. O. Silva, «Design for Manufacturing and Assembly (DFMA) of a Test Bench to Simulate Mechanical Vibrations in Rotating Equipment», ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 4B-2017, ene. 2018, doi:10.1115/IMECE2017-70892.; J. Blanco y Melgarejo Carlos, «DISEÑO Y MONTAJE DE UN BANCO DE PRUEBAS PARA DETECCIÓN Y CLASIFICACIÓN DE FALLAS EN RODAMIENTOS DE BOLA PARA MÁQUINAS ROTATIVAS», UNIVERSIDAD AUTONOMA DE BICARAMANGA, BUCARAMANGA, 2021.; M. Delgado, «Diseño y Construcción de kits de prueba para diagnosticar fallas en engranajes de máquinas rotativas», Universidad Autónoma de Bucaramanga, Bucaramanga, 2020.; K. Mendoza y A. Jaimes, «DISEÑO Y CONSTRUCCIÓN DE UN BANCO DE PRUEBAS MULTIFALLA PARA DIAGNOSTICO OFF-LINE EN MAQUINARIA ROTATIVA KAREN DARITZA MENDOZA CALDERÓN», UNIVERSIDAD AUTONOMA DE BUCARAMANGA, Bucaramanga, 2021.; M. Pico, «DISEÑO Y CONSTRUCCIÓN DE UN BANCO DE PRUEBAS PARA EL ANALISIS DE VIBRACIONES TORSIONALES», Universidad Autónoma de Bucaramanga, Bucaramanga, 2021.; «Rodamientos %7C SKF %7C SKF». Accedido: 14 de mayo de 2025. [En línea]. Disponible en: https://www.skf.com/co/products/rolling-bearings.; «¿Cuál es la estructura del rodamiento? La función de la estructura y sus componentes en la reducción de la fricción / Curiosidades sobre rodamientos». Accedido: 14 de mayo de 2025. [En línea]. Disponible en: https://koyo-jtekt-co-jp.translate.goog/en/bearing-column/bearing_trivia_1st_series/bearing_trivia_1st_series_03.html?_x_tr_sl=en&_x_tr_tl=es&_x_tr_hl=es&_x_tr_pto=tc.; «FFT». Accedido: 1 de junio de 2025. [En línea]. Disponible en: https://www.nti-audio.com/es/servicio/conocimientos/transformacion-rapida-de-fourier-fft.; «Defectos típicos en rodamientos y su identificación espectral %7C Power-MI». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://power-mi.com/es/content/defectos-t%C3%ADpicos-en-rodamientos-y-su-identificaci%C3%B3n-espectral.; ] «Ventana de Hamming %7C PDF %7C Análisis matemático %7C Enseñanza de matemática». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://es.scribd.com/document/451445077/VENTANA-DE-HAMMING-docx?utm_source=chatgpt.com.; «Entender la ventana de Hanning: una guía práctica para principiantes – Wray Castle». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://wraycastle.com/es/blogs/knowledge-base/hanning-window?srsltid=AfmBOorUf36HQr2ocf0YFfQQqFXogEaSiPsHT5v1yJSV4rjFteCsp889&utm_source=chatgpt.com.; F. Izaurieta, «Redes Neuronales Artificiales», Departamento de Fısica, Universidad de Concepcion, …, Accedido: 6 de junio de 2025. [En línea]. Disponible en: https://www.academia.edu/750527/Redes_Neuronales_Artificiales.; F. Lubinus Badillo, C. A. Rueda Hernández, B. Marconi Narváez, y Y. E. Arias Trillos, «Redes neuronales convolucionales: un modelo de Deep Learning en imágenes diagnósticas. Revisión de tema», Revista colombiana de radiología, vol. 32, n.o 3, pp. 5591-5599, sep. 2021, doi:10.53903/01212095.161.; «¿Qué son las redes neuronales convolucionales? %7C IBM». Accedido: 6 de junio de 2025. [En línea]. Disponible en: https://www.ibm.com/es-es/think/topics/convolutional-neural-networks.; «Cross-Spectral Density: Essential Key Concepts in Frequency Analysis». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://www.numberanalytics.com/blog/cross-spectral-density-fundamentals.; «What is the Cross Spectral Density (CSD)? - Vibration Research». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://vibrationresearch.com/blog/what-is-cross-spectral-density-csd/.; ] R. B. Randall y J. Antoni, «Rolling element bearing diagnostics—A tutorial», Mech Syst Signal Process, vol. 25, n.o 2, pp. 485-520, feb. 2011, doi:10.1016/J.YMSSP.2010.07.017.; «¿Qué es la densidad espectral de potencia (PSD)? - Vibración aleatoria». Accedido: 5 de junio de 2025. [En línea]. Disponible en: https://vru.vibrationresearch.com/lesson/what-is-the-psd/.; SKF, «Daño de rodamiento y análisis de fallas», feb. 2017.; C. Quintero, F. Merchán, A. Cornejo, y J. S. Galán, «Uso de Redes Neuronales Convolucionales para el Reconocimiento Automático de Imágenes de Macroinvertebrados para el Biomonitoreo Participativo», KnE Engineering, vol. 3, n.o 1, p. 585, feb. 2018, doi:10.18502/keg.v3i1.1462.; «¿Qué es un Rodamiento? %7C NSK Americas». Accedido: 11 de mayo de 2025. [En línea]. Disponible en: https://www.nsk.com/am-es/tools-resources/technical-services/training/whats-a-bearing/.; «Vista de Clasificador neuronal de fallos en rodamientos utilizando entradas basadas en transformadas wavelet packet y de Fourier». Accedido: 11 de mayo de 2025. [En línea]. Disponible en: https://revistas.udea.edu.co/index.php/ingenieria/article/view/16316/14140.; «Vista de Diagnóstico de fallas tempranas de rodamientos en mecanismos susceptibles al desbalanceo y a la desalineación %7C Revista UIS Ingenierías». Accedido: 11 de mayo de 2025. [En línea]. Disponible en: https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/9032/9120.; T. Ince et al., «Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks», IEEE Access, vol. 9, pp. 139260-139270, 2021, doi:10.1109/ACCESS.2021.3117603.; Z. Jia, Z. Liu, C. M. Vong, y M. Pecht, «A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images», IEEE Access, vol. 7, pp. 12348-12359, 2019, doi:10.1109/ACCESS.2019.2893331.; W. Zhao, C. Hua, D. Dong, y H. Ouyang, «A novel method for identifying crack and shaft misalignment faults in rotor systems under noisy environments based on CNN», Sensors (Switzerland), vol. 19, n.o 23, dic. 2019, doi:10.3390/s19235158.; A. Choudhary, T. Mian, S. Fatima, y B. K. Panigrahi, «Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions», IEEE Sens J, vol. 23, n.o 5, pp. 4628-4637, mar. 2023, doi:10.1109/JSEN.2022.3164430.; D. Neupane, Y. Kim, y J. Seok, «Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)», IEEE Access, vol. 9, pp. 88151-88166, 2021, doi:10.1109/ACCESS.2021.3089698.; X. Hao, Y. Zheng, L. Lu, y H. Pan, «Research on intelligent fault diagnosis of rolling bearing based on improved deep residual network», Applied Sciences (Switzerland), vol. 11, n.o 22, nov. 2021, doi:10.3390/app112210889.; L. Xin, S. Haidong, J. Hongkai, y X. Jiawei, «Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds», Struct Health Monit, vol. 21, n.o 2, pp. 339-353, mar. 2022, doi:10.1177/1475921721998957.; A. Dibaj, M. M. Ettefagh, R. Hassannejad, y M. B. Ehghaghi, «A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults», Expert Syst Appl, vol. 167, abr. 2021, doi:10.1016/j.eswa.2020.114094.; S. G. Kumbhar y E. Sudhagar P, «An integrated approach of Adaptive Neuro-Fuzzy Inference System and dimension theory for diagnosis of rolling element bearing», Measurement (Lond), vol. 166, dic. 2020, doi:10.1016/j.measurement.2020.108266.; W. Li, Z. Zhu, F. Jiang, G. Zhou, y G. Chen, «Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method», Mech Syst Signal Process, vol. 50-51, pp. 414-426, 2015, doi:10.1016/j.ymssp.2014.05.034.; https://apolo.unab.edu.co/en/persons/jessica-gissella-maradey-lazaro-2; https://hdl.handle.net/20.500.12749/30931; reponame:Repositorio Institucional UNAB; repourl:https://repository.unab.edu.co

Availability: https://hdl.handle.net/20.500.12749/30931

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

Authors: Hong-yu Shen Jia-lin Xu Zhen Zhu et al.

Source: Neoplasia: An International Journal for Oncology Research, Vol 45, Iss , Pp 100942- (2023)

Subject Terms: Antigen processing and presentation machinery, Gene signatures, Breast cancer, Risk assessment, Gene mutation, Immunotherapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1476558623000660; https://doaj.org/toc/1476-5586

Access URL: https://doaj.org/article/6f4cc1b43a754eadac40c8992725201f

Algorithms for Sparse Linear Systems

Authors: Jennifer Scott Miroslav Tůma

Resource Type: eBook.

Subjects: Sparse matrices, Linear systems, Algorithms

Categories: MATHEMATICS / Numerical Analysis, COMPUTERS / Computer Science, MATHEMATICS / Algebra / Linear, MATHEMATICS / Applied

Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization.

Authors: Sengupta, Nandana Udell, Madeleine Srebro, Nathan et al.

Source: Sociological Methodology; Feb2023, Vol. 53 Issue 1, p72-114, 43p

Subject Terms: MATRIX decomposition, MISSING data (Statistics), ARCHIVAL resources, HISTORICAL source material, SOCIAL scientists