Results 11 to 20 of about 2,291,439 (188)

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
doaj   +1 more source

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, Jiazhen Li, Weiqi Liu, Jinye Peng, Z. Jane Wang   +4 more
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Reionization constraints using Principal Component Analysis [PDF]

, 2010
Using a semi-analytical model developed by Choudhury & Ferrara (2005) we study the observational constraints on reionization via a principal component analysis (PCA).
Andrea Ferrara, Barkana, Bolton, Burigana, Chiu, Choudhury, Choudhury, Choudhury, Choudhury, Choudhury, Clarkson, Crittenden, Efstathiou, Efstathiou, Fan, Fan, Furlanetto, Gallerani, Gallerani, Gallerani, Hopkins, Hu, Huterer, Huterer, Kadota, Kashikawa, Lacey, Larson, Leach, Lewis, Loeb, Miralda-Escudé, Mortonson, Munshi, Press, Press, Pritchard, Prochaska, Schneider, Songaila, Sourav Mitra, T. Roy Choudhury, Totani, Wyithe   +43 more
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Euler Principal Component Analysis [PDF]

International Journal of Computer Vision, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stephan Liwicki, Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic   +3 more
openaire   +4 more sources

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
doaj   +1 more source

Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis [PDF]

ITM Web of Conferences
Nonlinear 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, Tenri Ampa Andi, Yahya Irma, La Ome Lilis, Saidi La Ode   +4 more
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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
doaj   +1 more source

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, SATYENDRA NATH MANDAL, PRITAM GHOSH, SUBHRANIL MUSTAFI, SANTANU BANIK   +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
openaire   +3 more sources

Generalized principal component analysis (GPCA) [PDF]

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003
This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace ...
Vidal, Rene, Ma, Yi, Sastry, Shankar
openaire   +3 more sources