Results 21 to 30 of about 2,300,147 (337)
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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Improved Two-Dimensional Quaternion Principal Component Analysis
IEEE Access, 2019 The two-dimensional quaternion principal component analysis (2D-QPCA) is first improved into abstracting the features of quaternion matrix samples in both row and column directions, being the generalization ability, and with the components weighted by ...
Meixiang Zhao, Zhigang Jia, Dunwei Gong
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Robust Orthogonal Complement Principal Component Analysis [PDF]
, 2016 Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation ...
Li, Shijie, She, Yiyuan, Wu, Dapeng
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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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Terrestrial, Atmospheric and Oceanic Sciences, 2017 The tropical Atlantic Ocean exhibits several modes of interannual variability such as the equatorial (or Atlantic Niño) mode, and meridional (or Atlantic dipole) mode.
S. C. Kenfack +6 more
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Robust Principal Component Analysis on Graphs [PDF]
, 2015 Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples.
Bresson, Xavier +4 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 Component Analysis of Infertility Data
Journal of Kufa for Mathematics and Computer, 2013 This paper applied PCA on infertility set of data, that was collected from Al-Nasiriya  province. Infertility of women that have been unable to conceive a child after one year of their marriage without birth control. Since infertility is very common
Nazera Khalil Dakhil +2 more
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Osmotic dehydration of fish: principal component analysis [PDF]
Acta Periodica Technologica, 2014 Osmotic treatment of the fish Carassius gibelio was studied in two osmotic solutions: ternary aqueous solution - S1, and sugar beet molasses - S2, at three solution temperatures of 10, 20 and 30oC, at atmospheric pressure. The aim was to examine
Lončar Biljana Lj. +6 more
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