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Relevant bibliographies by topics / Nonlinear PCA (NLPCA)

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Journal articles
Dissertations / Theses
Conference papers

Academic literature on the topic 'Nonlinear PCA (NLPCA)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Nonlinear PCA (NLPCA)"

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Kenfack, S. C., K. F. Mkankam, G. Alory, et al. "Sea surface temperature patterns in Tropical Atlantic: principal component analysis and nonlinear principal component analysis." Nonlinear Processes in Geophysics Discussions 1, no. 1 (2014): 235–67. http://dx.doi.org/10.5194/npgd-1-235-2014.

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Abstract. Principal Component Analysis (PCA) is one of the popular statistical methods for feature extraction. The neural network model has been performed on the PCA to obtain nonlinear principal component analysis (NLPCA), which allows the extraction of nonlinear features in the dataset missed by the PCA. NLPCA is applied to the monthly Sea Surface Temperature (SST) data from the eastern tropical Atlantic Ocean (29° W–21° E, 25° S–7° N) for the period 1982–2005. The focus is on the differences between SST inter-annual variability patterns; either extracted through traditional PCA or the NLPCA

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Bernacchia, A., P. Naveau, M. Vrac, and P. Yiou. "Detecting spatial patterns with the cumulant function – Part 2: An application to El Niño." Nonlinear Processes in Geophysics 15, no. 1 (2008): 169–77. http://dx.doi.org/10.5194/npg-15-169-2008.

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Abstract. The spatial coherence of a measured variable (e.g. temperature or pressure) is often studied to determine the regions of high variability or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA), are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly develope

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Kim, Jong-Min, and Il-Do Ha. "Deep Learning-Based Residual Control Chart for Binary Response." Symmetry 13, no. 8 (2021): 1389. http://dx.doi.org/10.3390/sym13081389.

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A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neura

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Jain, Meenal, and Gagandeep Kaur. "A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection." Journal of Computing and Information Technology 27, no. 4 (2020): 1–16. http://dx.doi.org/10.20532/cit.2019.1004591.

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Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely

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Lin, Jyh-Woei. "Ionospheric Precursor before a 588 km Deep Argentina’ Suncho Corral Earthquake on 28 May 2012, M=6.7 Using Both Principal Component Analysis (PCA) and Nonlinear Principal Component Analysis (NLPCA)." Journal of Earth Science Research, May 30, 2013, 25–34. http://dx.doi.org/10.18005/jesr0101005.

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Dissertations / Theses on the topic "Nonlinear PCA (NLPCA)"

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Scholz, Matthias. "Approaches to analyse and interpret biological profile data." Phd thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980988799.

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Conference papers on the topic "Nonlinear PCA (NLPCA)"

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Mukherjee, Joydeb, Venkataramana B. Kini, Sunil Menon, and Lalitha Eswara. "Gas Turbine Fault Detection and Diagnosis Using Nonlinear Feature Extraction Methods." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68802.

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Accurate gas turbine fault detection and diagnosis (FDD) is essential to improving airline safety as well as in reducing airline costs associated with delays and cancellations. In this paper, we present FDD methods based on feature extraction methods using nonlinear principal component analysis (NLPCA) and curvilinear component analysis (CCA). The underlying principle of both methods is to find the most representative feature space corresponding to gas turbine normal and faulty operations. During operation, new sensor data is located in this feature space and then it is determined whether a pa

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