Results 21 to 30 of about 232,203 (264)
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
Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. [PDF]
PLoS ONE, 2015 Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the
Nan Lin +3 more
doaj +1 more source
Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]
Jisuanji gongcheng, 2016 Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
doaj +1 more source
Land, 2022 The primary purpose of this study is to develop a method that can assist in exploring infrastructure-related multidimensional data. The spatial distribution of social infrastructure, including housing and service facilities, is usually uneven across a ...
Seong-Yun Hong +4 more
doaj +1 more source
Sparse principal component based high-dimensional mediation analysis [PDF]
Computational Statistics & Data Analysis, 2020 Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. With multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators.
Yi Zhao +2 more
openaire +4 more sources
Sparse Principal Component Analysis for Natural Language Processing [PDF]
Annals of Data Science, 2020 AbstractHigh dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text data. Those matrices are often sparse in the sense that they contain many zero elements.
Drikvandi, Reza, Lawal, Olamide
openaire +4 more sources
Weighted sparse principal component analysis
Chemometrics and Intelligent Laboratory Systems, 2019 Sparse principal component analysis (SPCA) has been shown to be a fruitful method for the analysis of high-dimensional data. So far, however, no method has been proposed that allows to assign elementwise weights to the matrix of residuals, although this may have several useful applications.
Van Deun, Katrijn +6 more
openaire +2 more sources
Approximation bounds for sparse principal component analysis [PDF]
Mathematical Programming, 2014 We produce approximation bounds on a semidefinite programming relaxation for sparse principal component analysis. These bounds control approximation ratios for tractable statistics in hypothesis testing problems where data points are sampled from Gaussian models with a single sparse leading component.
d'Aspremont, Alexandre +2 more
openaire +3 more sources
Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains [PDF]
Journal of Artificial Intelligence and Data Mining, 2020 In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important.
S. Mavaddati
doaj +1 more source
Principal Component Analysis With Sparse Fused Loadings [PDF]
Journal of Computational and Graphical Statistics, 2010 In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural "blocking" structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features
Jian, Guo +4 more
openaire +2 more sources