Добірка наукової літератури з теми "Dimensionality reduction analysis"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Dimensionality reduction analysis".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Dimensionality reduction analysis"
Vats, Deepak, and Avinash Sharma. "Dimensionality Reduction Techniques: Comparative Analysis." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2684–88. http://dx.doi.org/10.1166/jctn.2020.8967.
Повний текст джерелаXie, Fuding, Yutao Fan, and Ming Zhou. "Dimensionality Reduction by Weighted Connections between Neighborhoods." Abstract and Applied Analysis 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/928136.
Повний текст джерелаFujiwara, Takanori, Xinhai Wei, Jian Zhao, and Kwan-Liu Ma. "Interactive Dimensionality Reduction for Comparative Analysis." IEEE Transactions on Visualization and Computer Graphics 28, no. 1 (January 2022): 758–68. http://dx.doi.org/10.1109/tvcg.2021.3114807.
Повний текст джерелаMasram, M. S., and T. Diwan. "Microblog Dimensionality Reduction With Semantic Analysis." International Journal of Computer Sciences and Engineering 6, no. 1 (January 31, 2018): 342–46. http://dx.doi.org/10.26438/ijcse/v6i1.342346.
Повний текст джерелаLiang, Zhizheng, Shixiong Xia, and Yong Zhou. "Normalized discriminant analysis for dimensionality reduction." Neurocomputing 110 (June 2013): 153–59. http://dx.doi.org/10.1016/j.neucom.2012.12.007.
Повний текст джерелаSchott, James R. "Dimensionality reduction in quadratic discriminant analysis." Computational Statistics & Data Analysis 16, no. 2 (August 1993): 161–74. http://dx.doi.org/10.1016/0167-9473(93)90111-6.
Повний текст джерелаKumar, Aswani. "Analysis of unsupervised dimensionality reduction techniques." Computer Science and Information Systems 6, no. 2 (2009): 217–27. http://dx.doi.org/10.2298/csis0902217k.
Повний текст джерелаNgo, T. T., M. Bellalij, and Y. Saad. "The Trace Ratio Optimization Problem for Dimensionality Reduction." SIAM Journal on Matrix Analysis and Applications 31, no. 5 (January 2010): 2950–71. http://dx.doi.org/10.1137/090776603.
Повний текст джерелаWang, Shanshan, Lan Bai, Xu Chen, Zhen Wang, and Yuan-Hai Shao. "Divergent Projection Analysis for Unsupervised Dimensionality Reduction." Procedia Computer Science 199 (2022): 384–91. http://dx.doi.org/10.1016/j.procs.2022.01.047.
Повний текст джерелаYuan, Sen, Xia Mao, and Lijiang Chen. "Multilinear Spatial Discriminant Analysis for Dimensionality Reduction." IEEE Transactions on Image Processing 26, no. 6 (June 2017): 2669–81. http://dx.doi.org/10.1109/tip.2017.2685343.
Повний текст джерелаДисертації з теми "Dimensionality reduction analysis"
Khosla, Nitin, and n/a. "Dimensionality Reduction Using Factor Analysis." Griffith University. School of Engineering, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061010.151217.
Повний текст джерелаVamulapalli, Harika Rao. "On Dimensionality Reduction of Data." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.
Повний текст джерелаVasiloglou, Nikolaos. "Isometry and convexity in dimensionality reduction." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28120.
Повний текст джерелаCommittee Chair: David Anderson; Committee Co-Chair: Alexander Gray; Committee Member: Anthony Yezzi; Committee Member: Hongyuan Zha; Committee Member: Justin Romberg; Committee Member: Ronald Schafer.
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.
Повний текст джерелаRay, Sujan. "Dimensionality Reduction in Healthcare Data Analysis on Cloud Platform." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161375080072697.
Повний текст джерелаDi, Ciaccio Lucio. "Feature selection and dimensionality reduction for supervised data analysis." Thesis, Massachusetts Institute of Technology, 2016. https://hdl.handle.net/1721.1/122827.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 103-106).
by Lucio Di Ciaccio.
S.M.
S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
Coleman, Ashley B. "Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452775165.
Повний текст джерелаHui, Shirley. "FlexSADRA: Flexible Structural Alignment using a Dimensionality Reduction Approach." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1173.
Повний текст джерелаZhang, Yuyao. "Non-linear dimensionality reduction and sparse representation models for facial analysis." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0019/document.
Повний текст джерелаFace analysis techniques commonly require a proper representation of images by means of dimensionality reduction leading to embedded manifolds, which aims at capturing relevant characteristics of the signals. In this thesis, we first provide a comprehensive survey on the state of the art of embedded manifold models. Then, we introduce a novel non-linear embedding method, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination. The proposed algorithm successfully outperforms the traditional linear PCA transform to capture the salient features generated by different illuminations, and reconstruct the illuminated faces with high accuracy. We also consider the problem of automatically classifying human face poses from face views with varying illumination, as well as occlusion and noise. Based on the sparse representation methods, we propose two dictionary-learning frameworks for this pose classification problem. The first framework is the Adaptive Sparse Representation pose Classification (ASRC). It trains the dictionary via a linear model called Incremental Principal Component Analysis (Incremental PCA), tending to decrease the intra-class redundancy which may affect the classification performance, while keeping the extra-class redundancy which is critical for sparse representation. The other proposed work is the Dictionary-Learning Sparse Representation model (DLSR) that learns the dictionary with the aim of coinciding with the classification criterion. This training goal is achieved by the K-SVD algorithm. In a series of experiments, we show the performance of the two dictionary-learning methods which are respectively based on a linear transform and a sparse representation model. Besides, we propose a novel Dictionary Learning framework for Illumination Normalization (DL-IN). DL-IN based on sparse representation in terms of coupled dictionaries. The dictionary pairs are jointly optimized from normally illuminated and irregularly illuminated face image pairs. We further utilize a Gaussian Mixture Model (GMM) to enhance the framework's capability of modeling data under complex distribution. The GMM adapt each model to a part of the samples and then fuse them together. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images
Moraes, Lailson Bandeira de. "Two-dimensional extensions of semi-supervised dimensionality reduction methods." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12388.
Повний текст джерелаApproved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:02:06Z (GMT) No. of bitstreams: 2 Dissertaçao Lailson de Moraes.pdf: 4634910 bytes, checksum: cbec580f8cbc24cb3feb2379a1d2dfbd (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)
Made available in DSpace on 2015-03-13T13:02:06Z (GMT). No. of bitstreams: 2 Dissertaçao Lailson de Moraes.pdf: 4634910 bytes, checksum: cbec580f8cbc24cb3feb2379a1d2dfbd (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-08-19
An important pre-processing step in machine learning systems is dimensionality reduction, which aims to produce compact representations of high-dimensional patterns. In computer vision applications, these patterns are typically images, that are represented by two-dimensional matrices. However, traditional dimensionality reduction techniques were designed to work only with vectors, what makes them a suboptimal choice for processing two-dimensional data. Another problem with traditional approaches for dimensionality reduction is that they operate either on a fully unsupervised or fully supervised way, what limits their efficiency in scenarios where supervised information is available only for a subset of the data. These situations are increasingly common because in many modern applications it is easy to produce raw data, but it is usually difficult to label it. In this study, we propose three dimensionality reduction methods that can overcome these limitations: Two-dimensional Semi-supervised Dimensionality Reduction (2D-SSDR), Two-dimensional Discriminant Principal Component Analysis (2D-DPCA), and Two-dimensional Semi-supervised Local Fisher Discriminant Analysis (2D-SELF). They work directly with two-dimensional data and can also take advantage of supervised information even if it is available only for a small part of the dataset. In addition, a fully supervised method, the Two-dimensional Local Fisher Discriminant Analysis (2D-LFDA), is proposed too. The methods are defined in terms of a two-dimensional framework, which was created in this study as well. The framework is capable of generally describing scatter-based methods for dimensionality reduction and can be used for deriving other two-dimensional methods in the future. Experimental results showed that, as expected, the novel methods are faster and more stable than the existing ones. Furthermore, 2D-SSDR, 2D-SELF, and 2D-LFDA achieved competitive classification accuracies most of the time when compared to the traditional methods. Therefore, these three techniques can be seen as viable alternatives to existing dimensionality reduction methods.
Um estágio importante de pré-processamento em sistemas de aprendizagem de máquina é a redução de dimensionalidade, que tem como objetivo produzir representações compactas de padrões de alta dimensionalidade. Em aplicações de visão computacional, estes padrões são tipicamente imagens, que são representadas por matrizes bi-dimensionais. Entretanto, técnicas tradicionais para redução de dimensionalidade foram projetadas para lidar apenas com vetores, o que as torna opções inadequadas para processar dados bi-dimensionais. Outro problema com as abordagens tradicionais para redução de dimensionalidade é que elas operam apenas de forma totalmente não-supervisionada ou totalmente supervisionada, o que limita sua eficiência em cenários onde dados supervisionados estão disponíveis apenas para um subconjunto das amostras. Estas situações são cada vez mais comuns por que em várias aplicações modernas é fácil produzir dados brutos, mas é geralmente difícil rotulá-los. Neste estudo, propomos três métodos para redução de dimensionalidade capazes de contornar estas limitações: Two-dimensional Semi-supervised Dimensionality Reduction (2DSSDR), Two-dimensional Discriminant Principal Component Analysis (2D-DPCA), e Twodimensional Semi-supervised Local Fisher Discriminant Analysis (2D-SELF). Eles operam diretamente com dados bi-dimensionais e também podem explorar informação supervisionada, mesmo que ela esteja disponível apenas para uma pequena parte das amostras. Adicionalmente, um método completamente supervisionado, o Two-dimensional Local Fisher Discriminant Analysis (2D-LFDA) é proposto também. Os métodos são definidos nos termos de um framework bi-dimensional, que foi igualmente criado neste estudo. O framework é capaz de descrever métodos para redução de dimensionalidade baseados em dispersão de forma geral e pode ser usado para derivar outras técnicas bi-dimensionais no futuro. Resultados experimentais mostraram que, como esperado, os novos métodos são mais rápidos e estáveis que as técnicas existentes. Além disto, 2D-SSDR, 2D-SELF, e 2D-LFDA obtiveram taxas de erro competitivas na maior parte das vezes quando comparadas aos métodos tradicionais. Desta forma, estas três técnicas podem ser vistas como alternativas viáveis aos métodos existentes para redução de dimensionalidade.
Книги з теми "Dimensionality reduction analysis"
Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Знайти повний текст джерелаGeometric data analysis: An empirical approach to dimensionality reduction and the study of patterns. New York: Wiley, 2001.
Знайти повний текст джерелаKrämer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer Berlin / Heidelberg, 2013.
Знайти повний текст джерелаCarreira-Perpinan, Miguel A. Dimensionality Reduction (Chapman & Hall/Crc Computer Science & Data Analysis). Chapman & Hall/CRC, 2009.
Знайти повний текст джерелаKramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer, 2016.
Знайти повний текст джерелаPopov, Valentin, and Markus Heß. Method of Dimensionality Reduction in Contact Mechanics and Friction. Springer Berlin / Heidelberg, 2016.
Знайти повний текст джерелаPopov, Valentin L., and Markus Heß. Method of Dimensionality Reduction in Contact Mechanics and Friction. Springer, 2014.
Знайти повний текст джерелаPopov, Valentin L., and Markus Heß. Method of Dimensionality Reduction in Contact Mechanics and Friction. Springer, 2014.
Знайти повний текст джерелаKirby, Michael. Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns. Wiley & Sons, Incorporated, John, 2008.
Знайти повний текст джерелаKirby, Michael. Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns. Wiley-Interscience, 2000.
Знайти повний текст джерелаЧастини книг з теми "Dimensionality reduction analysis"
Durstewitz, Daniel. "Dimensionality Reduction." In Advanced Data Analysis in Neuroscience, 105–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59976-2_6.
Повний текст джерелаPhillips, Jeff M. "Dimensionality Reduction." In Mathematical Foundations for Data Analysis, 143–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_7.
Повний текст джерелаGisbrecht, Andrej, Daniela Hofmann, and Barbara Hammer. "Discriminative Dimensionality Reduction Mappings." In Advances in Intelligent Data Analysis XI, 126–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_13.
Повний текст джерелаHung, Chih-Cheng, Enmin Song, and Yihua Lan. "Dimensionality Reduction and Sparse Representation." In Image Texture Analysis, 103–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1_4.
Повний текст джерелаAndersson, Fredrik, and Jens Nilsson. "Nonlinear Dimensionality Reduction Using Circuit Models." In Image Analysis, 950–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_96.
Повний текст джерелаCampadelli, Paola, Elena Casiraghi, and Claudio Ceruti. "Neighborhood Selection for Dimensionality Reduction." In Image Analysis and Processing — ICIAP 2015, 183–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_17.
Повний текст джерелаWilliamson, Len. "Persistent Homology for Dimensionality Reduction." In Reinforcement Learning Algorithms: Analysis and Applications, 97–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_9.
Повний текст джерелаLi, Zuoling, and Guirong Weng. "GCM Data Analysis Using Dimensionality Reduction." In Advances in Computer Science and Education, 217–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27945-4_34.
Повний текст джерелаMoore, Jason H., and Peter C. Andrews. "Epistasis Analysis Using Multifactor Dimensionality Reduction." In Methods in Molecular Biology, 301–14. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2155-3_16.
Повний текст джерелаTripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "Comparative Analysis of Dimensionality Reduction Techniques." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization, 137–49. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-14.
Повний текст джерелаТези доповідей конференцій з теми "Dimensionality reduction analysis"
Underhill, David G., Luke K. McDowell, David J. Marchette, and Jeffrey L. Solka. "Enhancing Text Analysis via Dimensionality Reduction." In 2007 IEEE International Conference on Information Reuse and Integration. IEEE, 2007. http://dx.doi.org/10.1109/iri.2007.4296645.
Повний текст джерелаZhang, Lei, Peipei Peng, Xuezhi Xiang, and Xiantong Zhen. "Dimensionality reduction by supervised locality analysis." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351048.
Повний текст джерелаFeng Zheng, Na Chen, and Luoqing Li. "Semi-supervised Laplacian eigenmaps for dimensionality reduction." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635894.
Повний текст джерелаSugiyama, Masashi. "Local Fisher discriminant analysis for supervised dimensionality reduction." In the 23rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143958.
Повний текст джерелаVo, Nhat, Duc Vo, Subhash Challa, and Bill Moran. "Parametric subspace analysis for dimensionality reduction and classification." In 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938672.
Повний текст джерелаNarwane, Swati V., and Sudhir D. Sawarkar. "Dimensionality Reduction of Unbalanced Datasets: Principal Component Analysis." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544971.
Повний текст джерелаNarwane, Swati V., and Sudhir D. Sawarkar. "Dimensionality Reduction of Unbalanced Datasets: Principal Component Analysis." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544971.
Повний текст джерелаMigenda, Nico, and Wolfram Schenck. "Adaptive Dimensionality Reduction for Local Principal Component Analysis." In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2020. http://dx.doi.org/10.1109/etfa46521.2020.9212129.
Повний текст джерелаKazemipour, Abbas, and Shaul Druckmann. "Nonlinear Dimensionality Reduction Via Polynomial Principal Component Analysis." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646515.
Повний текст джерелаPeng, Jing, Stefan Robila, Wei Fan, and Guna Seetharaman. "Analysis of Chernoff criterion for linear dimensionality reduction." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5641971.
Повний текст джерелаЗвіти організацій з теми "Dimensionality reduction analysis"
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. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada488142.
Повний текст джерела