Results 31 to 40 of about 563,170 (163)

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., Bishop, Christopher M.   +1 more
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Application of Principal Component Analysis for Steel Material Components

Kurdistan Journal of Applied Research, 2022
In this research, we made use of the principal component analysis (PCA) technique, which is a multivariate statistical method that transforms a fixed number of correlated variables into a fixed number of orthogonal, uncorrelated axes known as principal ...
Miran Othman Tofiq, Kawa Muhammad Jamal Rasheed   +1 more
doaj   +3 more sources

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.
J, Luo, B, Hu, X T, Ling, R W, Liu
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Principal components' features of mid-latitude geomagnetic daily variation [PDF]

Annales Geophysicae, 2010
The ionospheric and magnetospheric current systems are responsible of the daily magnetic field changes. Recently, the Natural Orthogonal Components (NOC) technique has been applied to model the physical system responsible of the daily variation of the
P. De Michelis, P. De Michelis, R. Tozzi, G. Consolini   +3 more
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Mining gene expression data by interpreting principal components

BMC Bioinformatics, 2006
Background There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of
Mortazavi Ali, Trout Diane, King Brandon W, Roden Joseph C, Wold Barbara J, Hart Christopher E   +5 more
doaj   +1 more source

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components

Frontiers in Genetics, 2020
Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects.
Chenglong Yu, Chenglong Yu, Chenglong Yu, Guiyan Ni, Guiyan Ni, Guiyan Ni, Julius van der Werf, S. Hong Lee, S. Hong Lee   +8 more
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Dual Principal Component Pursuit [PDF]

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015
We consider the problem of learning a linear subspace from data corrupted by outliers. Classical approaches are typically designed for the case in which the subspace dimension is small relative to the ambient dimension. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such, it is ...
Tsakiris, Manolis C., Vidal, René
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Linear Dimensionality Reduction: What Is Better?

Data
This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’
Mohit Baliyan, Evgeny M. Mirkes
doaj   +1 more source

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
openaire   +2 more sources

Principal components of nuclear mass model residuals

Physics Letters B
Principal Component Analysis (PCA) is applied to the residuals of six widely used nuclear mass models to uncover systematic deviations and identify missing physical effects in theoretical nuclear mass predictions. By analyzing the principal components of
Y. Y. Huang, X. H. Wu
doaj   +1 more source