Results 21 to 30 of about 536,911 (259)
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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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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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
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Longitudinal functional principal component analysis [PDF]
Electronic Journal of Statistics, 2010 We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The
Greven, Sonja +3 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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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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Structured Functional Principal Component Analysis [PDF]
Biometrics, 2014 Summary Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics
Shou, Haochang +3 more
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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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Bilinear Probabilistic Principal Component Analysis [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2012 Probabilistic principal component analysis (PPCA) is a popular linear latent variable model for performing dimension reduction on 1-D data in a probabilistic manner. However, when used on 2-D data such as images, PPCA suffers from the curse of dimensionality due to the subsequently large number of model parameters.
Kwok, JT, Yu, PLH, Zhao, J
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Principal Components Analysis Utility in the Livestock Field
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 2016 Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets.
Ancuta Simona Rotaru +3 more
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