Results 31 to 40 of about 563,921 (264)
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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Interactive Principal Component Analysis [PDF]
2017 21st International Conference Information Visualisation (IV), 2017 Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and ...
Harri Siirtola +2 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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Probabilistic Principal Component Analysis [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1999 Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely ...
Tipping, Michael E. +1 more
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Integrated Principal Components Analysis
J. Mach. Learn. Res., 2018 Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individualistic analyses of a single data source. We develop a new unsupervised data integration method named Integrated Principal Components Analysis (iPCA), which is a model-based generalization of PCA and serves
Tiffany M. Tang, Genevera I. Allen
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Shakespeare and principal components analysis
Digital Scholarship in the Humanities, 2021 Abstract The use of principal components analysis (PCA) in literary studies was pioneered by John Burrows. From him it was adopted by the New Oxford Shakespeare project team, who used it to support their controversial attributions of parts of Arden of Faversham to Shakespeare and parts of the Henry VI trilogy to Marlowe.
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Principal independent component analysis [PDF]
IEEE Transactions on Neural Networks, 1999 Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available.
Jie Luo 0001 +3 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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Principal Component Projection Without Principal Component Analysis
CoRR, 2016 We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression.
Roy Frostig +3 more
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