Results 51 to 60 of about 563,170 (163)
Robust principal component analysis?
Journal of the ACM, 2011 This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly
Candès, Emmanuel J. +3 more
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Compressive principal component pursuit [PDF]
2012 IEEE International Symposium on Information Theory Proceedings, 2012 30 pages, 1 figure, preliminary version submitted to ISIT ...
Wright, John +3 more
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Using Principal Components Analysis in Program Evaluation: Some Practical Considerations
Journal of MultiDisciplinary Evaluation, 2006 Principal Components Analysis (PCA) is widely used by behavioral science researchers to assess the dimensional structure of data and for data reduction purposes.
J. Thomas Kellow
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Multispectral principal component imaging
Optics Express, 2003 We analyze a novel multispectral imager that directly measures the principal component features of an object. Optical feature extraction is studied for color face images, multi-spectral LANDSAT-7 images, and their grayscale equivalents. Blockwise feature extraction is performed that exploits both spatial and spectral correlation, with the goal of ...
Himadri, Pal, Mark, Neifeld
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Coordinating Principal Component Analyzers [PDF]
, 2002 Mixtures of Principal Component Analyzers can be used to model high dimensional data that lie on or near a low dimensional manifold. By linearly mapping the PCA subspaces to one global low dimensional space, we obtain a 'global' low dimensional coordinate sys- tem for the data.
Verbeek, Jakob +2 more
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Entropy, 2015 The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy ...
Shunfang Wang, Ping Liu
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Chemomertic Risk Assessment of Soil Pollution
Open Chemistry, 2019 In this study, an interpretation and modeling of the soil quality by monitoring data using an intelligent data analysis is presented. On an annual average, values of 12 soil surface chemical parameters as input variables were determined at 35 sampling ...
Nedyalkova Miroslava, Simeonov Vasil
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Targeted principal components regression
Journal of Multivariate Analysis, 2022 We propose a principal components regression method based on maximizing a joint pseudo-likelihood for responses and predictors. Our method uses both responses and predictors to select linear combinations of the predictors relevant for the regression, thereby addressing an oft-cited deficiency of conventional principal components regression.
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Mapping discourses through Hierarchical Clustering on Principal Components
Methodological InnovationsThis article proposes a methodological framework that combines a Q-concourse questionnaire with Multiple Factor Analysis and Hierarchical Clustering on Principal Components (MFA/HCPC) to derive discourse typologies from large-N survey data. The framework
Francesco Veri
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Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes
Revista Brasileira de Engenharia Agrícola e AmbientalThe objective of this study was to evaluate the spatial variability of soybean yield, carbon stock, and soil physical attributes using multivariate and geostatistical techniques.
Ricardo N. Buss +5 more
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