Results 11 to 20 of about 2,261,156 (339)
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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A principal component analysis in concrete design
Budownictwo o Zoptymalizowanym Potencjale Energetycznym, 2022 Over the last 200 years, ordinary concrete has evolved from four basic ingredient materials (gravel, sand, cement, and water) to multicomponent complex composites. The number and variety of the additives, admixtures, non-conventional aggregates, fillers,
Janusz Kobaka, Jacek Katzer
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Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction [PDF]
المجلة العراقية للعلوم الاحصائية, 2019 This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image ...
Assel Muslim Essa, Asmaa Ghalib Alrawi
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A Low-Complexity Quantum Principal Component Analysis Algorithm
IEEE Transactions on Quantum Engineering, 2022 In this article, we propose a low-complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of
Chen He +4 more
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Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis [PDF]
ITM Web of ConferencesNonlinear Principal Component Analysis (PRINCALS) is an extension of Principal Component Analysis (Linear), which can reduce the variables of mixed scale multivariable data (nominal, ordinal, interval, and ratio) simultaneously.
Makkulau +4 more
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Principal component analysis in pig breeds identification
Indian Journal of Animal Sciences, 2023 Maintaining the purity of pig breeds is an essential task for their economic value. The traditional breed identification methods through coat colour are prone to error due to huge intra-breed variation. This paper uses principal component Analysis (PCA)
SANKET DAN +4 more
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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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Quantifying Topographic Ruggedness Using Principal Component Analysis
Advances in Civil Engineering, 2021 The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications.
Maan Habib
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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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Robust Principal Component Analysis on Graphs [PDF]
, 2015 Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples.
Bresson, Xavier +4 more
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