Results 41 to 50 of about 1,131,696 (312)
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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International Journal of Secondary Metabolite, 2017 In this study, we investigated the effects of cold pressed oil on physicochemical properties of milk thistle (Silybum marianum), anise seed (Pimpinella anisum), fennel seed (Foeniculum vulgare), terebinth (Pistacia terebinthus), coriander (Coriandrum ...
Zeliha Üstün Argon +3 more
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Classification of bottled waters marketed and consumed in Algeria through statistical approaches
Journal of Water and Health, 2023 The main objective of this work consists of classifying 30 brands of water bottled and marketed in Algeria, based on their physicochemical compositions and their comparison with some foreign brands recognized in their countries or on a world scale ...
Khadidja Ketrouci +4 more
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EFFICIENCY OF SOME CLASSIFICATION METHODS ADOPTED IN CLINICAL REMINDER SYSTEMS IN CASES OF MIXED DATA [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2008 Cluster analysis techniques are widely used in medical researches. Clustering techniques are not unique and hence users must be extremely conscious about what to use in order to analyze their data.
Imad H.A. AL-Iathary, Murtadhah M.H
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Scaled PCA: A New Approach to Dimension Reduction
Management Sciences, 2019 This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target ...
Dashan Huang +4 more
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Statistics & Risk Modeling, 2012 Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1,...,X n onto the first D eigenvectors of the Principal Component Analysis ...
Biau, Gérard, Mas, André
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Electronic Journal of Plant Breeding, 2021 The present research focuses on the identification of stable trait specific genetic resources across the five semiarid environments located in the four states of India.
M. Y. Dudhe1*, H. P. Meena1 , M. Sujatha1 , S. B. Sakhre2 , M. K. Ghodke3 , A. M. Misal3 , S. Neelima4 , V. V. Kulkarni5 , Praduman Yadav1 , A. R. G. Ranganatha1 and A. Vishnuvardhan Reddy1
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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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Geochemical evaluation of land use at a medieval harbor site in Masuda City, Chugoku region, Japan
Proceedings of the Mongolian Academy of Sciences, 2019 A large-scale Medieval harbor site has been recently discovered at Nakazu-Higashihara in Masuda City, Chugoku region, Japan. The Medieval harbor site is divided into north and south areas.
Dalai Banzragch +2 more
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Optimality and Sub-optimality of PCA I: Spiked Random Matrix Models [PDF]
Annals of Statistics, 2018 A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix.
Amelia Perry +3 more
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