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
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HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis [PDF]
Genomics & Informatics, 2020 In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results.
Nan Jiang, Sungyoung Lee, Taesung Park
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Principal component analysis (PCA) on temporal changes of soil health indicators
Ilmu Pertanian (Agricultural Science), 2023 Soil health indicators are related to environmental factors, such as nutrient management, crop practices, different cropping systems, and biodiversity. 14 soil health indicators were measured and compared in our study to clarify the impact of different ...
Fakher Kardoni
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Facial recognition system using eigenfaces and PCA [PDF]
Mathematics and Computational Sciences, 2023 Face recognition is an essential field of image processing and computer vision. In this paper, we have developed a facial recognition system that can detect and recognize the face of a person by comparing the characteristics, and features of the face to ...
Hamid Yazdani, Ali Shojaeifard
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Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
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Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor [PDF]
, 2012 This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor.
Gu, Fengshou, Ball, Andrew, Ahmed, M.
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GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis
IEEE Open Journal of Signal Processing, 2020 Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2-norm, which can be highly influenced by outlier data.
Breton Minnehan +2 more
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OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data
Metabolites, 2021 Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores.
Hiroyuki Yamamoto +2 more
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SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis
iScience, 2023 Summary: Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from
Dong Yuan, Nicholas Mancuso
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Principal Component Analysis (PCA) applied to the statistical control of multivariate processes
Investigación e Innovación en Ingenierías, 2022 Objective: to propose the analysis and monitoring of a chemical process sustained in the theoretical principles of a factorial method cataloged as Principal Component Analysis (PCA), whose ultimate objective is to represent the original variables of the
Juan carlos Herrera Vega +2 more
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