Relevant bibliographies by topics / Data Dimensionality Reduction

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

Academic literature on the topic 'Data Dimensionality Reduction'

Author: Grafiati
Published: 5 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Data Dimensionality Reduction"

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K, Bhargavi. "Data Dimensionality Reduction Techniques : Review." International Journal of Engineering Technology and Management Sciences 4, no. 4 (2020): 62–65. http://dx.doi.org/10.46647/ijetms.2020.v04i04.010.

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Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which inc
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Nagabhushan, P., K. Chidananda Gowda, and Edwin Diday. "Dimensionality reduction of symbolic data." Pattern Recognition Letters 16, no. 2 (1995): 219–23. http://dx.doi.org/10.1016/0167-8655(94)00085-h.

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K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.

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Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro ar
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Mahadev, Preeti, and P. Nagabhushan. "Incremental Dimensionality Reduction in Hyperspectral Data." International Journal of Computer Applications 163, no. 7 (2017): 21–34. http://dx.doi.org/10.5120/ijca2017913575.

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Sanguinetti, Guido. "Dimensionality Reduction of Clustered Data Sets." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 3 (2008): 535–40. http://dx.doi.org/10.1109/tpami.2007.70819.

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Villalón, Elena. "High-Dimensionality Data Reduction with Java." Computing in Science & Engineering 10, no. 5 (2008): 64–69. http://dx.doi.org/10.1109/mcse.2008.134.

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., Smita J. Khelukar. "HIGH DIMENSIONALITY REDUCTION ON GRAPHICAL DATA." International Journal of Research in Engineering and Technology 04, no. 11 (2015): 177–79. http://dx.doi.org/10.15623/ijret.2015.0411029.

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Gámez, A. J., C. S. Zhou, A. Timmermann, and J. Kurths. "Nonlinear dimensionality reduction in climate data." Nonlinear Processes in Geophysics 11, no. 3 (2004): 393–98. http://dx.doi.org/10.5194/npg-11-393-2004.

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Abstract. Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that makes the selection of a proper minimum number of subspaces for successfully representing the variability of the process ambiguous. The use of nonlinear methods can improve the embedding of multivariate data into lower dimensional manifolds. In this article, a nonlinear method for dimensionality reduction, Isomap, is applied to the sea
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Gisbrecht, Andrej, and Barbara Hammer. "Data visualization by nonlinear dimensionality reduction." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, no. 2 (2015): 51–73. http://dx.doi.org/10.1002/widm.1147.

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Li, Hongda, Jian Cui, Xinle Zhang, Yongqi Han, and Liying Cao. "Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction." Remote Sensing 14, no. 18 (2022): 4579. http://dx.doi.org/10.3390/rs14184579.

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Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods ca
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Dissertations / Theses on the topic "Data Dimensionality Reduction"

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Vamulapalli, Harika Rao. "On Dimensionality Reduction of Data." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.

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Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality red
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Widemann, David P. "Dimensionality reduction for hyperspectral data." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8448.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Baldiwala, Aliakbar. "Dimensionality Reduction for Commercial Vehicle Fleet Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38330.

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A variety of new features have been added in the present-day vehicles like a pre-crash warning, the vehicle to vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various electronic control units present inside the automotive transmit useful information via automotive multiplexing. Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. Optimum functionality is achieved by transmitting this data in real time. The high bandwidth and high-
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DWIVEDI, SAURABH. "DIMENSIONALITY REDUCTION FOR DATA DRIVEN PROCESS MODELING." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069770129.

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XU, NUO. "AGGRESSIVE DIMENSIONALITY REDUCTION FOR DATA-DRIVEN MODELING." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178640357.

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Law, Hiu Chung. "Clustering, dimensionality reduction, and side information." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science & Engineering, 2006.<br>Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 296-317). Also issued in print.
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Ross, Ian. "Nonlinear dimensionality reduction methods in climate data analysis." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492479.

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Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These hnear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three s
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Ray, Sujan. "Dimensionality Reduction in Healthcare Data Analysis on Cloud Platform." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161375080072697.

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Ha, Sook Shin. "Dimensionality Reduction, Feature Selection and Visualization of Biological Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77169.

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Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function.
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Di, Ciaccio Lucio. "Feature selection and dimensionality reduction for supervised data analysis." Thesis, Massachusetts Institute of Technology, 2016. https://hdl.handle.net/1721.1/122827.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 103-106).<br>by Lucio Di Ciaccio.<br>S.M.<br>S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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Books on the topic "Data Dimensionality Reduction"

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Garzon, Max, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, and Lih-Yuan Deng, eds. Dimensionality Reduction in Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9.

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Wang, Jianzhong. Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27497-8.

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service), SpringerLink (Online, ed. Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Springer Berlin Heidelberg, 2011.

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Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer Berlin Heidelberg, 2013.

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Barrett, Philip James. Exploratory database visualisation: The application & assessment of data and dimensionality reduction. Aston University. Department of Computer Science and Applied Mathematics, 1995.

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Nonlinear Dimensionality Reduction. Springer New York, 2010.

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Deng, Lih-Yuan. Dimensionality Reduction in Data Science. Springer International Publishing AG, 2023.

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Dimensionality Reduction in Data Science. Springer International Publishing AG, 2022.

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Multilabel Dimensionality Reduction. CRC Press, 2012.

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Ye, Jieping, Shuiwang Ji, and Liang Sun. Multi-Label Dimensionality Reduction. Taylor & Francis Group, 2016.

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Book chapters on the topic "Data Dimensionality Reduction"

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Sarang, Poornachandra. "Dimensionality Reduction." In Thinking Data Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-02363-7_2.

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Mathar, Rudolf, Gholamreza Alirezaei, Emilio Balda, and Arash Behboodi. "Dimensionality Reduction." In Fundamentals of Data Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56831-3_4.

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Chepushtanova, Sofya, Elin Farnell, Eric Kehoe, Michael Kirby, and Henry Kvinge. "Dimensionality Reduction." In Data Science for Mathematicians. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429398292-7.

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Dinov, Ivo D. "Dimensionality Reduction." In Data Science and Predictive Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_6.

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Durstewitz, Daniel. "Dimensionality Reduction." In Advanced Data Analysis in Neuroscience. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59976-2_6.

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Oskolkov, Nikolay. "Dimensionality Reduction." In Applied Data Science in Tourism. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88389-8_9.

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Phillips, Jeff M. "Dimensionality Reduction." In Mathematical Foundations for Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_7.

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Kavzoglu, Taskin, Brandt Tso, and Paul M. Mather. "Dimensionality Reduction." In Classification Methods for Remotely Sensed Data, 3rd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003439172-3.

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Vlachos, Michail. "Dimensionality Reduction." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_71-1.

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Vlachos, Michail. "Dimensionality Reduction." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_71.

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Conference papers on the topic "Data Dimensionality Reduction"

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Qian, Li, Claudia Plant, Yalan Qin, Jing Qian, and Christian Böhm. "DynoGraph: Dynamic Graph Construction for Nonlinear Dimensionality Reduction." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00100.

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Zhang, Juexin, Mingwen Li, Haoyuan Lu, and Xiaoguang Jiao. "Persymmetric Target Detection with Data Dividing Dimensionality Reduction." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618645.

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Wang, Jing, Xiaofeng Li, Xiaogang Dong, Jingsong Li, and Yongqi Li. "A Data Dimensionality Reduction Algorithm for Aerospace Telemetry Data Mining." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743381.

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Tang, Wen, Chong Zhang, Jiwei Yang, Jibing Wu, and Hongbin Huang. "Updatable Spatial Learned Index Based on Dimensionality Reduction." In 2024 10th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2024. https://doi.org/10.1109/bigdia63733.2024.10808934.

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Bunte, Kerstin, Michael Biehl, and Barbara Hammer. "Dimensionality reduction mappings." In 2011 Ieee Symposium On Computational Intelligence And Data Mining - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cidm.2011.5949443.

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Bingham, Ella, Aristides Gionis, Niina Haiminen, Heli Hiisilä, Heikki Mannila, and Evimaria Terzi. "Segmentation and dimensionality reduction." In Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.33.

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Zhang, Daoqiang, Zhi-Hua Zhou, and Songcan Chen. "Semi-Supervised Dimensionality Reduction." In Proceedings of the 2007 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.73.

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Penalver, M., F. Del Frate, M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza. "Onboard payload-data dimensionality reduction." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127069.

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Wei, Jia, Jiabing Wang, Qianli Ma, and Xuan Wang. "Adaptive Semi-Supervised Dimensionality Reduction." In 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.20.

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Pratap, Rameshwar, Raghav Kulkarni, and Ishan Sohony. "Efficient Dimensionality Reduction for Sparse Binary Data." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622338.

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Reports on the topic "Data Dimensionality Reduction"

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Nichols, Jonathan M., Frank Bucholtz, and Joseph V. Michalowicz. Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada507109.

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Ho, Tu Bao. Methods of Sparse Modeling and Dimensionality Reduction to Deal with Big Data. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada623178.

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Oskolkov, Nikolay. Dimension Reduction Methods for Life Sciences. Instats Inc., 2024. http://dx.doi.org/10.61700/gyxh9ued08xio1347.

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This seminar provides a comprehensive overview of dimension reduction techniques in R and Python for high-dimensional biological data, focusing on their practical applications in life sciences. Participants will gain both theoretical knowledge and practical experience in linear and nonlinear dimensionality reduction methods such as tSNE and UMAP, enhancing their ability to analyze complex datasets effectively. By the conclusion of the seminar, participants will understand the theoretical and practical foundations of these methods, with a wealth of examples that can be rapidly applied for their
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Bucholtz, Frank, Jonathan M. Nichols, Michael D. Duncan, and Leslie N. Smith. The Feasibility of Nonlinear Dimensionality Reduction for the Rapid Analysis of Persistent Surveillance Data, including the Detection of IED Placement Activity. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada488142.

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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/ywg7hgz3gf12y469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/oma5ikdj8xru1469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/nkdwj37n3trpc469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/8851t6mqarw95469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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