Results 1 to 10 of about 50,718 (252)

Craniofacial similarity analysis through sparse principal component analysis.

PLoS ONE, 2017
The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery.
Junli Zhao, Fuqing Duan, Zhenkuan Pan, Zhongke Wu, Jinhua Li, Qingqiong Deng, Xiaona Li, Mingquan Zhou   +7 more
doaj   +2 more sources

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis [PDF]

Scientific Reports, 2022
Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error).
Mohammad Mamouei, Yajie Zhu, Milad Nazarzadeh, Abdelaali Hassaine, Gholamreza Salimi-Khorshidi, Yutong Cai, Kazem Rahimi   +6 more
doaj   +2 more sources

Incorporating biological information in sparse principal component analysis with application to genomic data [PDF]

BMC Bioinformatics, 2017
Background Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data.
Ziyi Li, Sandra E. Safo, Qi Long
doaj   +2 more sources

Lymphocyte–monocyte–neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis [PDF]

Virology Journal, 2021
Background It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed.
Yingjie Qi, Jian-an Jia, Huiming Li, Nagen Wan, Shuqin Zhang, Xiaoling Ma   +5 more
doaj   +2 more sources

Determination of optimal sampling wavelengths for reflectance measurement using sparse principal component analysis [PDF]

Scientific Reports
In the visible region, material reflectance spectra are typically recorded over the 400–700 nm range at uniformly spaced wavelength intervals (e.g., 1, 5, 10, or 20 nm).
Seyed Ali Amirshahi, Seyed Hossein Amirshahi, Hengameh Shirzadi   +2 more
doaj   +2 more sources

Retraction: Craniofacial similarity analysis through sparse principal component analysis [PDF]

PLoS ONE
PLOS One Editors
doaj   +3 more sources

Demixed Sparse Principal Component Analysis Through Hybrid Structural Regularizers

IEEE Access, 2021
Recently, the sparse representation of multivariate data has gained great popularity in real-world applications like neural activity analysis. Many previous analyses for these data utilize sparse principal component analysis (SPCA) to obtain a sparse ...
Yan Zhang, Haoqing Xu
doaj   +1 more source

JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit

BMC Bioinformatics, 2021
Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David, Chris S. Avery, Donald J. Jacobs   +2 more
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Class-Specific Sparse Principal Component Analysis for Visual Classification

IEEE Access, 2020
Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the
Fei Pan, Zai-Xu Zhang, Bao-Di Liu, Ji-Jun Xie   +3 more
doaj   +1 more source

Evaluating carbon neutrality potential in China based on sparse principal component analysis

Energy Reports, 2023
The resource endowments, development levels and emission reduction potential of different provinces in China are different. Accurately judging regional differences of carbon neutrality potential of each province is helpful to analyze the low-carbon ...
YuKun Liu, Xiyan Li, Xiqiao Lin
doaj   +1 more source