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Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
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Investigation of Morphological Diversity and Evaluation of Tomato Lines Yield Using Multivariate Statistical Analysis [PDF]
Majallah-i ̒Ulum-i Bāghbānī, 2022 Introduction Tomato is a product with a wide range of genotypes with different yields and selection based on this trait and its components can accelerate the breeding programs of this plant.
S. Golcheshmeh +3 more
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Identification of dietary patterns by principal component analysis in schoolchildren in the South of Brazil and associated factors [PDF]
Revista Brasileira de Saúde Materno Infantil, 2020 Objectives: to identify dietary patterns (DP) and associated factors in first grade school-children in elementary schools in the South of Brazil. Methods: school-based cross-sectional study, with a non-probabilistic sample of 782 schoolchildren aged 6 ...
Gabriela Rodrigues Bratkowski +3 more
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ANOVA bootstrapped principal components analysis for logistic regression
Croatian Review of Economic, Business and Social Statistics, 2022 Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal components is selected to be used in a classification or a regression model to boost accuracy.
Toleva Borislava
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Recursive principal components analysis [PDF]
Neural Networks, 2005 A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by
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Applied Univariate, Bivariate, and Multivariate Statistics Using Python, 2018 Examples: – Clustering: partition data into groups of similar/nearby points. – Dimensionality reduction: data often lies near a low-dimensional subspace (or manifold) in feature space; matrices have low-rank approximations.
Stu Daultrey
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Wishart Mechanism for Differentially Private Principal Components Analysis [PDF]
AAAI Conference on Artificial Intelligence, 2015 We propose a new input perturbation mechanism for publishing a covariance matrix to achieve (epsilon,0)-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise.
Wuxuan Jiang, Cong Xie, Zhihua Zhang
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COPD phenotype description using principal components analysis
Respiratory Research, 2009 Background Airway inflammation in COPD can be measured using biomarkers such as induced sputum and FeNO. This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal ...
Vestbo Jørgen +5 more
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Remote Sensing, 2022 Recently, Jia et al. employed the index, modified remote sensing ecological index (MRSEI), to evaluate the ecological quality of the Qaidam Basin, China. The MRSEI made a modification to the previous remote sensing-based ecological index (RSEI), which is
Hanqiu Xu +3 more
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A principal component analysis for trees
The Annals of Applied Statistics, 2009 The active field of Functional Data Analysis (about understanding the variation in a set of curves) has been recently extended to Object Oriented Data Analysis, which considers populations of more general objects. A particularly challenging extension of this set of ideas is to populations of tree-structured objects.
Aydın, Burcu +4 more
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