Results 11 to 20 of about 744,201 (267)
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 ...
Christopher M Bishop
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Modal Principal Component Analysis [PDF]
Neural Computation, 2020 Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Keishi Sando, Hideitsu Hino
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A Generalization of Principal Component Analysis [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem.
Samuele Battaglino, Erdem Koyuncu
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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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Euler Principal Component Analysis [PDF]
International Journal of Computer Vision, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stephan Liwicki +3 more
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Extended Principal Component Analysis
CoRR, 2021 Principal Component Analysis (PCA) is a transform for finding the principal components (PCs) that represent features of random data. PCA also provides a reconstruction of the PCs to the original data. We consider an extension of PCA which allows us to improve the associated accuracy and diminish the numerical load, in comparison with known techniques ...
Pablo Soto-Quiros, Anatoli Torokhti
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Principal component and factor analysis to study variations in the aging lumbar spine [PDF]
, 2015 Human spine is a multifunctional structure of human body consisting of bones, joints, ligaments and muscles which all undergo a process of change with the age. A sudden change in these features either naturally or thorough injury can lead to some serious
Shah, Akeel A. +4 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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Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure [PDF]
, 2013 A fundamental problem in structural damage detection is to define an efficient feature to calculate a damage index. Furthermore, due to perturbations from various sources, we also need to define a rigorous threshold whose overtaking indicates the ...
HAJRYA, Rafik, MECHBAL, Nazih
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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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