Results 21 to 30 of about 386,750 (261)
APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO DROUGHT INDICATORS OF THREE REPRESENTATIVE CROATIAN REGIONS [PDF]
e-GFOS, 2021 Drought has become a very frequent hydrological event globally, including in Croatia. It can generally be explained by air temperature and precipitation changes on an annual and seasonal basis, owing to climate change.
Lidija Tadić +3 more
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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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Classification and analysis of the MNIST dataset using PCA and SVM algorithms
Vojnotehnički Glasnik, 2023 Introduction/purpose: The utilization of machine learning methods has become indispensable in analyzing large-scale, complex data in contemporary data-driven environments, with a diverse range of applications from optimizing business operations to ...
Mokhaled N. A. Al-Hamadani
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Acta Agriculturae Slovenica, 2020 The ‘Žutica’ represents the most common Montenegrin olive varieties mainly used for the production of olive oil and green and black fruit canning.
Mirjana ADAKALIĆ +5 more
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Principal Component Analysis Based Wavelet Transform [PDF]
Engineering and Technology Journal, 2012 The principal component analysis (PCA) is a valuable statistical means, implemented in time domain that has found application in many fields such as face recognition and image compression, and is a common technique for finding patterns in data of high ...
Hana M. Salman
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RKF-PCA: Robust kernel fuzzy PCA
Neural Networks, 2009 Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components.
Computer and Information Science and Engineering, University of Florida, United States ( host institution ) +3 more
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, 2023 Phylogenetic PCA (p-PCA) is a version of PCA for observations that are leaf nodes of a phylogenetic tree. P-PCA accounts for the fact that such observations are not independent, due to shared evolutionary history. The method works on Euclidean data, but in evolutionary biology there is a need for applying it to data on manifolds, particularly shapes ...
Morten Akhøj +2 more
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Ruhuna Journal of Science, 2022 Stream sediment samples play an important role in identifying potential areas of metallic and non-metallic mineralization in mineral exploration studies.
Aref Shirazi +4 more
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Helminthologia, 2022 Aphelenchoides bicaudatus associated with grass in South Africa was identified morphologically and molecularly. This population is characterized by a body length of 409 – 529 μm, a stylet length of 9.5 – 13 μm, a post-vulval uterine sac of 45 – 50 μm ...
Shokoohi E., Moyo N. A. G.
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Dimensionality Reduction: Challenges and Solutions [PDF]
ITM Web of Conferences, 2022 The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between
Ahmad Noor, Nassif Ali Bou
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