Relevant bibliographies by topics / PCA ALGORITHM

Contents

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

Academic literature on the topic 'PCA ALGORITHM'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "PCA ALGORITHM"

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Angela, Nadya, and Robertus Setiawan Aji Nugroho. "COMPARISON BETWEEN DEEP NEURAL NETWORK AND PRINCIPAL COMPONENT ANALYSIS ALGORITHM IN FACE RECOGNITION." Proxies : Jurnal Informatika 5, no. 1 (2024): 50–63. http://dx.doi.org/10.24167/proxies.v5i1.12445.

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Face recognition is one technology that is commonly used now. Therefore, various algorithms continue to be developed to obtain maximum results with minimum costs. One of them is the Deep Neural Network or DNN algorithm. While DNN requires a large dataset to train the algorithm, another algorithm called the Principal Component Analysis (PCA) algorithm works good in a smaller dataset. These algorithms are compared to know which algorithm has the better result in given circumstances. Later the accuracy, speed, and optimality of the algorithms are analyzed. By comparing these algorithms, we could
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Dinanti, Aldila, and Joko Purwadi. "Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis." Jambura Journal of Mathematics 5, no. 1 (2023): 155–65. http://dx.doi.org/10.34312/jjom.v5i1.17098.

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This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reducti
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Subiyanto, Subiyanto, Dina Priliyana, Moh Eki Riyadani, Nur Iksan, and Hari Wibawanto. "Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (2020): 210–16. http://dx.doi.org/10.14710/jtsiskom.2020.13590.

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Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay ci
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Zhang, Kang, Yongdong Huang, and Cheng Zhao. "Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (2018): 1850037. http://dx.doi.org/10.1142/s0219691318500376.

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In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for
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Hadiprakoso, Raden Budiarto, and I. Komang Setia Buana. "Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition." IJICS (International Journal of Informatics and Computer Science) 5, no. 3 (2021): 250. http://dx.doi.org/10.30865/ijics.v5i3.3333.

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Face recognition is a highly active research topic in pattern recognition and computer vision, with numerous practical applications. Face recognition can provide the most natural interaction experience similar to the way humans can recognize others. This paper presents a performance comparison of various machine learning approaches and feature extraction algorithms. The feature extraction algorithm used is Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and a combination of PCA-LDA. The method used is to take a dataset sample and then evaluate and compare machine learnin
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MR., VRISHABH J. SHAH, SHAILESH S. PENKAR MR., and DEEPA MANOJ MRS. "PCA: THE DETECTION OF INTUITIVENESS." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 3 (2017): 92–94. https://doi.org/10.5281/zenodo.1462705.

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The study of science brain continually receives information which it processes, evaluates, and compare to the stored information and makes appropriate decisions. This technology serves to detect information in the brain using P300 mermer algorithm along with pattern classification algorithm as a means of detecting the attention, information processing, and memory-related responses to these presentations as revealed by brain waves with the help of biological neural network. https://journalnx.com/journal-article/20150195
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Dolan, Matthew T., Sung Kim, Yu-Hsuan Shao, and Grace L. Lu-Yao. "Authentication of Algorithm to Detect Metastases in Men with Prostate Cancer Using ICD-9 Codes." Epidemiology Research International 2012 (August 22, 2012): 1–7. http://dx.doi.org/10.1155/2012/970406.

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Background. Metastasis is a crucial endpoint for patients with prostate cancer (PCa), but currently lacks a validated claims-based algorithm for detection. Objective. To develop an algorithm using ICD-9 codes to facilitate accurate reporting of PCa metastases. Methods. Medical records from 300 men hospitalized at Robert Wood Johnson University Hospital for PCa were reviewed. Using the presence of metastatic PCa on chart review as the gold standard, two algorithms to detect metastases were compared. Algorithm A used ICD-9 codes 198.5 (bone metastases), 197.0 (lung metastases), 197.7 (liver meta
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Zhao, Wenjing, Yue Chi, Yatong Zhou, and Cheng Zhang. "Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1259703.

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SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental
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Subramaniam, Ashwin, and Byung-Joo Oh. "Mushroom Recognition Using PCA Algorithm." International Journal of Software Engineering and Its Applications 10, no. 1 (2016): 43–50. http://dx.doi.org/10.14257/ijseia.2016.10.1.05.

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Li, Mingfei, Zhengpeng Chen, Jiangbo Dong, et al. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems." Energies 15, no. 7 (2022): 2556. http://dx.doi.org/10.3390/en15072556.

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In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diag
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Dissertations / Theses on the topic "PCA ALGORITHM"

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Petters, Patrik. "Development of a Supervised Multivariate Statistical Algorithm for Enhanced Interpretability of Multiblock Analysis." Thesis, Linköpings universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138112.

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In modern biological research, OMICs techniques, such as genomics, proteomics or metabolomics, are often employed to gain deep insights into metabolic regulations and biochemical perturbations in response to a specific research question. To gain complementary biologically relevant information, multiOMICs, i.e., several different OMICs measurements on the same specimen, is becoming increasingly frequent. To be able to take full advantage of this complementarity, joint analysis of such multiOMICs data is necessary, but this is yet an underdeveloped area. In this thesis, a theoretical background
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Ergin, Emre. "Investigation Of Music Algorithm Based And Wd-pca Method Based Electromagnetic Target Classification Techniques For Their Noise Performances." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611218/index.pdf.

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Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres.
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Romualdo, Kamilla Vogas. "Problemas direto e inverso de processos de separação em leito móvel simulado mediante mecanismos cinéticos de adsorção." Universidade do Estado do Rio de Janeiro, 2012. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=6750.

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Diversas aplicações industriais relevantes envolvem os processos de adsorção, citando como exemplos a purificação de produtos, separação de substâncias, controle de poluição e umidade entre outros. O interesse crescente pelos processos de purificação de biomoléculas deve-se principalmente ao desenvolvimento da biotecnologia e à demanda das indústrias farmacêutica e química por produtos com alto grau de pureza. O leito móvel simulado (LMS) é um processo cromatográfico contínuo que tem sido aplicado para simular o movimento do leito de adsorvente, de forma contracorrente ao movimento do líquido,
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the a
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Wang, Xuechuan, and n/a. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030619.162803.

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Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Princi
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Wang, Xuechuan. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365680.

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Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Princi
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Rimal, Suraj. "POPULATION STRUCTURE INFERENCE USING PCA AND CLUSTERING ALGORITHMS." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2860.

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Genotype data, consisting large numbers of markers, is used as demographic and association studies to determine genes related to specific traits or diseases. Handling of these datasets usually takes a significant amount of time in its application of population structure inference. Therefore, we suggested applying PCA on genotyped data and then clustering algorithms to specify the individuals to their particular subpopulations. We collected both real and simulated datasets in this study. We studied PCA and selected significant features, then applied five different clustering techniques to obtai
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Katadound, Sachin. "Face Recognition: Study and Comparison of PCA and EBGM Algorithms." TopSCHOLAR®, 2004. http://digitalcommons.wku.edu/theses/241.

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Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analy
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Perez, Gallardo Jorge Raúl. "Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10505/1/perez_gallardo_partie_1_sur_2.pdf.

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Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environm
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Lacasse, Alexandre. "Bornes PAC-Bayes et algorithmes d'apprentissage." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27635/27635.pdf.

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L’objet principale de cette thèse est l’étude théorique et la conception d’algorithmes d’apprentissage concevant des classificateurs par vote de majorité. En particulier, nous présentons un théorème PAC-Bayes s’appliquant pour borner, entre autres, la variance de la perte de Gibbs (en plus de son espérance). Nous déduisons de ce théorème une borne du risque du vote de majorité plus serrée que la fameuse borne basée sur le risque de Gibbs. Nous présentons également un théorème permettant de borner le risque associé à des fonctions de perte générale. À partir de ce théorème, nous concevons des a
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Books on the topic "PCA ALGORITHM"

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Karakatič, Sašo, and Iztok Fister ml. Strojno učenje: S Pythonom do prvega klasifikatorja. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.feri.1.2022.

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Knjiga služi kot uvod v področje strojnega učenja za vse, ki imajo vsaj osnovne izkušnje s programiranjem. Pregledajo se pomembni pojmi strojnega učenja (model znanja, učna in testna množica, algoritem učenja), natančneje pa se predstavi tehnika klasifikacije in način ovrednotenja kvalitete modelov znanja klasifikacije. Spozna se algoritem klasifikacije k najbližjih sosedov in predstavi se uporaba tega algoritma – tako konceptualno kakor v programski kodi. Knjiga poda številne primere v programskem jeziku Python in okolju Jupyter Notebooks. Za namen utrjevanja znanja pa so ponujene naloge (tak
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Coates, Laura C., Arthur Kavanaugh, and Christopher T. Ritchlin. Treatment algorithm and treat to target. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0032.

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This chapter covers the evidence for treatment algorithms and treatment to target in PsA. Evidence for treatment algorithms including step up vs step down approaches to prescribing and early vs delayed treatment is discussed. EULAR recommendations for treating to target in SpA are outlined with a summary of the level of evidence available at that time. Key outcome measures that could be utilized as targets in PsA are reviewed with discussion of their merits and deficiencies. A detailed description of the first treat to target study in PsA is presented: the TICOPA study. The impact of comorbidi
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Fiedler, Klaus, and Florian Kutzner. Pseudocontingencies. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.14.

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In research on causal inference and in related paradigms (conditioning, cue learning, attribution), it has been traditionally taken for granted that the statistical contingency between cause and effect drives the cognitive inference process. However, while a contingency model implies a cognitive algorithm based on joint frequencies (i.e., the cell frequencies of a 2 x 2 contingency table), recent research on pseudocontingencies (PCs) suggests a different mental algorithm that is driven by base rates (i.e., the marginal frequencies of a 2 x 2 table). When the base rates of two variables are ske
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Capps, Carlos H. Setup reduction in PCB assembly: A group technology application using Genetic Algorithms. 1997.

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Fragoulis, George E., and Iain B. McInnes. Small molecules in the treatment of psoriatic arthritis. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0031.

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Psoriatic arthritis (PsA) is a chronic inflammatory arthritis, occurring in about one third of psoriasis patients, and exhibiting very varied clinical manifestations and comorbidities. Although the clinical outcome of the disease has been significantly improved recently, mainly due to utilization of novel agents targeting the IL-23/-17 axis, unmet needs still exist. Emerging insights into the disease’s pathogenesis led to development of new drugs acting against critical molecular targets and their efficacy in psoriasis and/or PsA has been tested in Phase III clinical trials. Some of these ther
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Pittenger, Arthur O. An Introduction to Quantum Computing Algorithms (Progress in Computer Science and Applied Logic (PCS)). Birkhäuser Boston, 2001.

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Pazzi, Annalisa. Applicazioni Di Grafi e Algoritmi Alla Fuga Di Pac-Man Dal Ghosts Team: Codice Completo in Linguaggio C. Independently Published, 2020.

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AlJaroudi, Wael. Myocardial Perfusion Imaging Before and After Cardiac Revascularization. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0015.

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Coronary artery disease (CAD) remains the leading cause of morbidity and mortality worldwide. While the burden of the disease remains high, the rates of death attributable to CAD have declined by almost a third between 1998 and 2008. In patients with stable ischemic heart disease (SIHD), data supporting survival benefit from coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) versus no revascularization are outdated with the recent advancement in medical therapy. Over the years, myocardial perfusion imaging (MPI) has played a significant role in detecting is
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Michael, Blair, Walker George, and Willey Stuart, eds. Financial Markets and Exchanges Law. 3rd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/law/9780198827528.001.0001.

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The book provides a comprehensive and authoritative analysis on the regulation of financial markets and market infrastructure. It focuses on stock markets and exchanges, associated trading, clearing, and settlement, and on payment systems, set in their historical and current contexts. This new edition addresses a number of major developments that have impacted the UK, wider European and international financial markets, such as within the UK, the PRA, the FCA and the Bank of England have become established financial regulators, each with its distinguishing responsibilities; MiFID has been subst
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Book chapters on the topic "PCA ALGORITHM"

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Tao, Yang, and Yuanzi He. "Improved PCA Face Recognition Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7981-3_44.

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Verboven, Sabine, Peter J. Rousseeuw, and Mia Hubert. "An improved algorithm for robust PCA." In COMPSTAT. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_67.

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Ding, Shifei, Weikuan Jia, Chunyang Su, Xinzheng Xu, and Liwen Zhang. "PCA-Based Elman Neural Network Algorithm." In Advances in Computation and Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92137-0_35.

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Salgado, Paulo, and Getúlio Igrejas. "A PCA-Fuzzy Clustering Algorithm for Contours Analysis." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24001-0_28.

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Zhang, Haigang, Yixin Yin, Sen Zhang, and Changyin Sun. "An Improved ELM Algorithm Based on PCA Technique." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14066-7_10.

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Draganov, Ivo, Roumen Kountchev, and Veska Georgieva. "Medical Images Transform by Multistage PCA-Based Algorithm." In Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00029-9_8.

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Das, Debasis, and Rajiv Misra. "A Parallel AES Encryption Algorithm Based on PCA." In Advances in Parallel Distributed Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24037-9_23.

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Wen, Shicheng, Shijin Yuan, Bin Mu, and Hongyu Li. "Robust PCA-Based Genetic Algorithm for Solving CNOP." In Intelligent Computing Theories and Methodologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22180-9_59.

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Babu, Mylam Chinnappan, and Sangaralingam Pushpa. "Genetic Algorithm-Based PCA Classification for Imbalanced Dataset." In Intelligent Computing in Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2780-7_59.

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Chang, W. H., and M. C. Cheng. "Image Retrieval by Auto Weight Regulation PCA Algorithm." In Intelligent Systems Design and Applications. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44999-7_37.

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Conference papers on the topic "PCA ALGORITHM"

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Rabbani, Shaik, V. Venkata Rao, and Suneetha Bobbillapati. "Implementation of Heart Disease Prediction Using PCA Classification Algorithm." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870832.

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Zhao, Guoqing, Ran An, Chunyue Cui, Wenjia Huang, Qi Yao, and Hongyan Yang. "A Pump Fault Warning Algorithm Based on Improved PCA-KNN." In 2024 International Conference on Intelligent Robotics and Automatic Control (IRAC). IEEE, 2024. https://doi.org/10.1109/irac63143.2024.10871892.

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Kurniawan, Rahmad, Nuraina Suhada, Sukamto, Tisha Melia, Zailani, and Joko Risanto. "Optimizing Preacher Assignments Using AHC-PCA Algorithm in Kampar Regency." In 2024 IEEE 2nd International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT). IEEE, 2024. https://doi.org/10.1109/iceecit63698.2024.10859845.

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Dagher, Issam. "Incremental PCA-LDA algorithm." In 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2010. http://dx.doi.org/10.1109/cimsa.2010.5611752.

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Salgado, Paulo, Lio Gonçalves, Getúlio Igrejas, et al. "Sliding PCA Fuzzy Clustering Algorithm." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637005.

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Feng, Xu, and Wenjian Yu. "A Fast Adaptive Randomized PCA Algorithm." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/411.

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It is desirable to adaptively determine the number of dimensions (rank) for PCA according to a given tolerance of low-rank approximation error. In this work, we aim to develop a fast algorithm solving this adaptive PCA problem. We propose to replace the QR factorization in randQB_EI algorithm with matrix multiplication and inversion of small matrices, and propose a new error indicator to incrementally evaluate approximation error in Frobenius norm. Combining the shifted power iteration technique for better accuracy, we finally build up an algorithm named farPCA. Experimental results show that
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Wang, Qianqian, Quanxue Gao, Xinbo Gao, and Feiping Nie. "Angle Principal Component Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/409.

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Recently, many ℓ1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed. Angle PCA employs ℓ2-norm to measure reconstruction error and variance of projected da-ta and maximizes the summation of ratio between variance and reconstruction error of each data. Angle PCA not only
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Hasanbelliu, Erion, Luis Sanchez Giraldo, and Jose C. Principe. "A Recursive Online Kernel PCA Algorithm." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.50.

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Yu, Wenjian, Yu Gu, Jian Li, Shenghua Liu, and Yaohang Li. "Single-Pass PCA of Large High-Dimensional Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/468.

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Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the top singular vectors of the data matrix) becomes a challenging task. In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data. It is suitable for processing extremely large and high-dimensional data stored in slow memory (hard disk) or the data generated in a streaming fashion. Experiments with synthetic and real data validate the algorithm's accuracy, which has orde
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Bansal, Abhishek, Kapil Mehta, and Sahil Arora. "Face Recognition Using PCA and LDA Algorithm." In Communication Technologies (ACCT). IEEE, 2012. http://dx.doi.org/10.1109/acct.2012.52.

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Reports on the topic "PCA ALGORITHM"

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis
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Poovendran, Radha, and Brian Matt. Security Analysis and Extensions of the PCB Algorithm for Distributed Key Generation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada459087.

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Kottke, Albert, Norman Abrahamson, David Boore, et al. Selection of Random Vibration Procedures for the NGA-East Project. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2018. http://dx.doi.org/10.55461/ltmu9309.

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Pseudo-spectral acceleration (PSA) is the most commonly used intensity measure in earthquake engineering as it serves as a simple approximate predictor of structural response for many types of systems. Therefore, most ground-motion models (GMMs, aka GMPEs) provide median and standard deviation PSA using a suite of input parameters characterizing the source, path, and site effects. Unfortunately, PSA is a complex metric: the PSA for a single oscillator frequency depends on the Fourier amplitudes across a range of frequencies. The Fourier amplitude spectrum (FAS) is an appealing alternative beca
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Suriyaphol, Gunnaporn. Study the gene expression of E-cadherin, syndecan1, matrix metalloproteinases-2, -7, -9, -14 and tissue inhibitors of metalloproteinases-1 and -2 in canine oral melanoma. Chulalongkorn University, 2015. https://doi.org/10.58837/chula.res.2015.80.

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The objectives of this study were to 1.) select the suitable reference genes for quantitative real-time polymerse chain reaction in the most common canine oral cancers: oral melanoma (OM) and oral squamous cell carcinoma (OSCC), 2.) study the gene expression of E-cadherin (CDH1), syndecan 1 (SDC1), matrix metalloproteinases-2, -7, -9, -14 (MMP2, MMP7, MMP9, MMP14) and tissue inhibitors of metalloproteinases-1 and -2 (TIMP1, TIMP2) in canine OM at the mRNA level and study the CDH1, SDC1 and Ki-67 protein expression by immunohistochemistry, and 3.) study the association of gene expression and th
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Panek. PR-312-12208-R01 Plume Volume Molar Ratio Method Assumptions and Conservative Model Over-Predictions. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010806.

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With the introduction of the more stringent short-term 1-hour air quality NO2 standard, the use of redundant, overly conservative assumptions (e.g. use of permit allowable emissions � hourly rate based on maximum annual tons per year) is no longer appropriate and potentially misinforms and obfuscates modeling compliance demonstrations. One of the challenges of modeling oxides of nitrogen (NOx) emissions is determining the amount of total NOx that will exist in the form of nitrogen dioxide (NO2) at a receptor. Combustion source emissions usually contain mostly nitric oxide (NO), which is not a
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