Results 41 to 50 of about 2,186,647 (234)
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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Principal Component Analysis of Munich Functional Developmental Diagnosis
Pediatric Reports, 2021 Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life.
Grażyna Pazera +3 more
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Simplicial Nonlinear Principal Component Analysis [PDF]
, 2012 We present a new manifold learning algorithm that takes a set of data points lying on or near a lower dimensional manifold as input, possibly with noise, and outputs a simplicial complex that fits the data and the manifold.
Hunt, Thomas, Krener, Arthur J.
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Manifold Regularized Principal Component Analysis Method Using L2,p-Norm
Mathematics, 2022 The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples.
Minghua Wan +3 more
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N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
core
Principal Component Analysis in ECG Signal Processing
EURASIP Journal on Advances in Signal Processing, 2007 This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained.
Roig José Millet +4 more
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Principal Component Analysis on Recurrent Venous Thromboembolism
Clinical and Applied Thrombosis/Hemostasis, 2019 The rates of recurrent venous thromboembolism (RVTE) vary widely, and its causes still need to be elucidated. Statistical multivariate methods can be used to determine disease predictors and improve current methods for risk calculation.
Tiago D. Martins PhD +3 more
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Low-Light Image Enhancement by Principal Component Analysis
IEEE Access, 2019 Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components.
Steffi Agino Priyanka +2 more
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Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis
Remote Sensing, 2020 Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent ...
Alina L. Machidon +4 more
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Data Exploration Using Tableau and Principal Component Analysis
JOIV: International Journal on Informatics Visualization, 2022 This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling.
Hanna Arini Parhusip +4 more
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