Results 21 to 30 of about 527,772 (339)
International Journal of Food Properties, 2021 Aerial parts of Artemisia judaica from Jordan were subjected to different drying methods, including shade (ShD), sun (SD), oven (OD) drying at different temperatures in addition to microwave drying (MWD).
Mahmoud A. Al-Qudah +7 more
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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 ...
Serena Bradde +2 more
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Study of population structure and stratification two ecotypes buffalo with dense single nucleotide polymorphism markers using Admixture, MDS, PCA and GC methods [PDF]
مجله بیوتکنولوژی کشاورزی, 2016 In applications of population genetics, classification of individuals in a sample into populations is important. With the development of high throughput genotyping technologies many markers such as SNPs are available which useful in the study of genetic ...
Zahra Azizi +4 more
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Variational Autoencoders Pursue PCA Directions (by Accident) [PDF]
Computer Vision and Pattern Recognition, 2018 The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance.
Michal Rolinek +2 more
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Experimental investigation of 50 MPa reinforced concrete slabs subjected to blast loading
Ingeniería e Investigación, 2018 This paper presents results from blast tests conducted on four 50 MPa concrete slabs with reinforcement ratios of 0,175% and 0,37%. Two of the slabs were retrofitted with 50 mm thick foam in order to investigate the potential of using the foam as a ...
Fausto Mendonça +2 more
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Feature selected based on PCA and optimized LMC [PDF]
MATEC Web of Conferences, 2021 In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into ...
Xi Ke, Cai Cheng
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Psychometrika, 2019 It is well known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition.
Eldén, Lars, Trendafilov, Nickolay
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Physicochemical characterization of the unconfined water-table aquifer of the Fez-Meknes basin [PDF]
E3S Web of Conferences, 2021 This paper aims to determine the hydrogeochemical characteristics of the Fez-Meknes free water table thought the monitoring of the physicochemical analyses of nine samples during the year of 2013, 2016, and 2019. This water table, which circulates in the
El Fakir Rabia +4 more
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Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery [PDF]
IEEE Signal Processing Magazine, 2017 Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation.
Namrata Vaswani +3 more
semanticscholar +1 more source
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, 2011 Eigenvectors of data matrices play an important role in many computational problems, ranging from signal processing to machine learning and control. For instance, algorithms that compute positions of the nodes of a wireless network on the basis of pairwise distance measurements require a few leading eigenvectors of the distances matrix.
Satish Babu Korada +2 more
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