N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
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Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
core +4 more sources
Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor [PDF]
, 2012 This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor.
Gu, Fengshou, Ball, Andrew, Ahmed, M.
core +3 more sources
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
doaj +2 more sources
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
doaj +1 more source
Assessing the spatiotemporal dynamics of maize yield in the central and northern regions of Ukraine
Agrology, 2019 This paper aims to establish the regularities of the spatio-temporal variability of maize yield in the Polissya and Forest-steppe zones of Ukraine, identify the factors that have the greatest impact on the yield of maize and to carry out zoning of the ...
A. A. Zymaroieva, T. P. Fedonyuk
doaj +1 more source
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
doaj +1 more source
KPCA over PCA to assess urban resilience to floods [PDF]
E3S Web of Conferences, 2021 Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to ...
Satour Narjiss +3 more
doaj +1 more source
Principal component analysis of texture features derived from FDG PET images of melanoma lesions
EJNMMI Physics, 2022 Background The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the “bouncing beta” phenomenon which could in part explain why in a similar patient population texture ...
DeLeu Anne-Leen +7 more
doaj +1 more source
Horticulturae, 2022 A total of 124 identical volatile aromatic compounds were identified during storage of the European ‘Conference’ and the Asian ‘Yali’ pear cultivars in different temperature conditions. Only 5 volatiles were statistically differentiated in both cultivars
Jan Goliáš +2 more
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