Results 21 to 30 of about 576,910 (192)
PCA in autocorrelation space [PDF]
Object recognition supported by user interaction for service robots, 2003 The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task.
Popovici, V., Thiran, J.
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Journal of Statistical Physics, 2017 A system with many degrees of freedom can be characterized by a covariance matrix; principal components analysis (PCA) focuses on the eigenvalues of this matrix, hoping to find a lower dimensional description. But when the spectrum is nearly continuous, any distinction between components that we keep and those that we ignore becomes arbitrary; it then ...
Bradde, Serena, Bialek, William
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Comparison of temporal and spatial patterns of water quality parameters in Anzali Wetland (southwest of the Caspian Sea) using Support vector machine model [PDF]
Caspian Journal of Environmental Sciences, 2021 Urgent is growing to have reliable information from the country's water resources. In recent years, data mining models such as artificial neural network (ANN), gene expression programming, Bayesian network, machine algorithms, such as a support vector ...
Maryam Fallah +3 more
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Validation of Nonlinear PCA [PDF]
Neural Processing Letters, 2012 Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied ...
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DWT/PCA face recognition using automatic coefficient selection [PDF]
, 2008 In PCA-based face recognition, there is often a trade-off between selecting the most relevant parts of a face image for recognition and not discarding information which may be useful.
Paul Nicholl +3 more
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$e$PCA: High dimensional exponential family PCA
The Annals of Applied Statistics, 2018 Many applications, such as photon-limited imaging and genomics, involve large datasets with noisy entries from exponential family distributions. It is of interest to estimate the covariance structure and principal components of the noiseless distribution.
Liu, Lydia T. +2 more
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Principal component analysis and its generalizations for any type of sequence (PCA-Seq)
Вавиловский журнал генетики и селекции, 2020 In the 1940s, Karhunen and Loève proposed a method for processing a one-dimensional numeric time series by converting it into multidimensional by shifts. In fact, a one-dimensional number series was decomposed into several orthogonal time series.
V. M. Efimov +2 more
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Indonesian Journal of Chemistry, 2022 Orthosiphon aristatus (called kumis kucing in Indonesia) is a valuable herb for diabetes mellitus treatment. In this study, LC-MS/MS and PCA analyses were used to investigate the metabolite profile, classify O.
Faizal Maulana +5 more
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Gas chromatography-mass spectrometry in the taxonomy of Miscanthus
Вавиловский журнал генетики и селекции, 2020 Chemotaxonomy as a system approach deals with intra- and interspecific polymorphism of a group of taxa in order to clarify their taxonomic positions or to select material for selection or introduction.
N. M. Slynko +9 more
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SIAM Journal on Imaging Sciences, 2013 We describe a method for analyzing the shape variability of images, called geometric PCA. Our approach is based on the use of deformation operators to model the geometric variability of images around a reference mean pattern. This leads to a new algorithm for estimating shape variability.
Bigot, Jérémie +2 more
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