Results 31 to 40 of about 2,259,407 (341)

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
openaire   +3 more sources

RSEI or MRSEI? Comment on Jia et al. Evaluation of Eco-Environmental Quality in Qaidam Basin Based on the Ecological Index (MRSEI) and GEE. Remote Sens. 2021, 13, 4543

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, Weifang Duan, Wenhui Deng, Mengjing Lin   +3 more
doaj   +1 more source

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, Crainiceanu, Ciprian, Caffo, Brian, Reich, Daniel   +3 more
openaire   +3 more sources

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
doaj   +1 more source

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, Zipunnikov, Vadim, Crainiceanu, Ciprian M., Greven, Sonja   +3 more
openaire   +4 more sources

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
openaire   +4 more sources

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, Ioana Pop, Anamaria Vatca, Luisa Andronie   +3 more
doaj   +1 more source

A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition

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
doaj   +1 more source

Principal Components Analysis of EEG Signals for Epileptic Patient Identification

Computation, 2021
According to the behavior of its neuronal connections, it is possible to determine if the brain suffers from abnormalities such as epilepsy. This disease produces seizures and alters the patient’s behavior and lifestyle.
Maria Camila Guerrero, Juan Sebastián Parada, Helbert Eduardo Espitia   +2 more
doaj   +1 more source

Interpretable Functional Principal Component Analysis

Biometrics, 2015
SummaryFunctional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations ...
Lin, Zhenhua, Wang, Liangliang, Cao, Jiguo   +2 more
openaire   +2 more sources