Applied Surface Science Advances, 2023 Principal component analysis (PCA), as applied to the processing of the complex X-ray photoelectron spectroscopy (XPS) lineshapes, is discussed. PCA analysis example is provided of complex native iron oxide films on Fe foil XPS spectra further modified ...
Neal Fairley +3 more
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Sparse logistic principal components analysis for binary data [PDF]
, 2010 We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua +2 more
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Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach [PDF]
PeerJ, 2023 Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring.
José Eduardo Teixeira +7 more
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Principal Component Analysis in an Asymmetric Norm [PDF]
, 2014 Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different names.
Haerdle, Wolfgang Karl +2 more
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Data Restoration by Linear Estimation of the Principal Components From Lossy Data
IEEE Access, 2020 In this article, we propose a method based on principal component analysis (PCA) to restore data after the occurrence of data loss due to sensor defects or environmental factors.
Yonggeol Lee, Sang-Il Choi
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Principal Component Analysis (PCA)-Supported Underfrequency Load Shedding Algorithm [PDF]
Energies, 2020 This research represents a conceptual shift in the process of introducing flexibility into power system frequency stability-related protection. The existing underfrequency load shedding (UFLS) solution, although robust and fast, has often proved to be incapable of adjusting to different operating conditions.
Tadej Skrjanc +2 more
openaire +3 more sources
An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition [PDF]
, 2010 This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals.
Trendafilova, I.
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Integrating Neutrosophic Logic into Principal Component Analysis: A Python-Based Framework [PDF]
Neutrosophic Sets and SystemsPrincipal Component Analysis (PCA) is a widely used dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated principal components. However, classical PCA assumes precise and crisp data, which may not hold
D. Vidhya, S. Jafari, G. Nordo
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International Journal for Simulation and Multidisciplinary Design Optimization, 2023 Accurately predicting stock returns can help reduce market risk. This paper briefly introduced the long short-term memory (LSTM) algorithm model for predicting stock returns and combined it with principal component analysis (PCA) to improve the ...
Mi Yanxiang, Xu Donghai, Gao Tielin
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Representing complex data using localized principal components with application to astronomical data [PDF]
, 2007 Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex ...
A Gersho +43 more
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