NSTL国家科技图书文献中心

1. Principal component analysis of multivariate spatial functional data NSTL国家科技图书文献中心

Si-ahmed, Idris | Hamdad, Leila ... - 《Big Data Research》 - 2025,39 - 共12页

摘要： Functional Principal* Component Analysis (SMFPCA), which* | performs principal* component analysis for multivariate* | applies spectral functional component* analysis to* | This paper is devoted to the study of | dimension reduction techniques for multivariate spatially

关键词： Spectral analysis | Functional data analysis | Functional principal component analysis | Spatial-functional principal component | analysis | Multivariate analysis

2. Near-optimal quantum kernel principal component analysis NSTL国家科技图书文献中心

Wang, Youle - 《Quantum Science and Technology》 - 2025,10(1) - 1～17 - 共17页

摘要：Kernel principal* component analysis (kernel* | the extraction of informative principal* components* | learning, enabling effective analysis* of input data* | PCA) is a nonlinear dimensionality reduction | technique that employs kernel functions to map data into a

关键词： quantum computing | machine learning | kernel method | kernel principal component analysis

3. Tensor Robust Principal Component Analysis with Hankel Structure NSTL国家科技图书文献中心

Chao Xu | Hao Tan ... - 《Pattern Recognition and Computer Vision,Part I》 - Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 125～138 - 共14页

摘要：Tensor Robust Principal Component Analysis | (TRPCA) aims to recover clean tensor data corrupted | with noise. This method finds significant application | in recovering multidimensional data. However, the | majority of existing methods rely solely on local

关键词： Non-local similarity | Hankel structure | Tensor robust principal component analysis | Tensor nuclear norm | Alternating direction method of multiplier

4. Non-Decreasing Concave Regularized Minimization for Principal Component Analysis NSTL国家科技图书文献中心

Qinghai Zheng | Yixin Zhuang - 《IEEE signal processing letters》 - 2025,32 - 486～490 - 共5页

摘要：, Principal* Component Analysis (PCA) performs both the* | As a widely used method in signal processing | compression and the recovery of high dimensional data by | leveraging the linear transformations. Considering the | robustness of PCA, how to discriminate correct samples and

关键词： Principal component analysis | Minimization | Optimization | Signal processing algorithms | Robustness | High dimensional data | Dimensionality reduction | Lagrangian functions | Iterative algorithms | Adaptation models

5. Multivariate Optimization of Electrochemical Struvite Precipitation from Wastewater Using Principal Component Analysis NSTL国家科技图书文献中心

Alisha Zafar | Muhil Raj Prabhakar ... - 《ACS - ES & T Water》 - 2025,5(5) - 2361～2372 - 共12页

摘要： component analysis(PCA)to reduce the key infuential | aims to investigate the ability of principal | -based anode.The result was evaluated using principal | principal factors are intricately interconnected,resulting | Electrocoagulation can enhance struvite

关键词： ammonium | phosphorus | struvite | electrocoagulation | principal component analysis

6. Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease NSTL国家科技图书文献中心

Jiahui Feng | Haolun Shi ... - 《Statistics in medicine.》 - 2025,44(3/4) - e10324～e10324 - 共1页

摘要： component analysis (sFPCA) method to extract image | . We propose a novel supervised functional principal | ABSTRACT Brain imaging data is one of the | primary predictors for assessing the risk of Alzheimer's | disease (AD). This study aims to extract image‐based

关键词： functional principal component analysis | image analysis | survival analysis | triangulation

7. A novel method to enhance medical image reconstruction using Genetic Algorithm and Incremental Principal Component Analysis NSTL国家科技图书文献中心

Onur T.O. - 《Computers in Biology and Medicine》 - 2025,185 - Article 109527～Article 109527 - 共10页

摘要： Component Analysis (IPCA). The proposed method aims to | Genetic Algorithm (GA) with Incremental Principal | © 2024Medical imaging has an crucial role in | modern healthcare and helps diagnosing and treating for | a variety of medical conditions. However, the

关键词： Genetic Algorithm | Image reconstruction | Incremental Principal Component Analysis | MRI

8. Exploration of suitable utilization paths for different parts of corn straw based on principal component analysis NSTL国家科技图书文献中心

Fu, Guohao | Huo, Lili ... - 《Biomass Conversion and Biorefinery》 - 2025,15(4) - 6013～6026 - 共14页

摘要： determined using principal* component *analysis, and the | As a widely produced renewable, low-cost | biomass resource, corn straw's physicochemical | properties and utilization potential have been extensively | studied in order to utilize it with high value and

关键词： Corn straw | Segmented utilization | Index system | Principal component analysis

9. Robust principal component analysis combined with top-hat transform for clutter suppression in GPR images NSTL国家科技图书文献中心

Fang Ye | Rui Zhang ... - 《Remote sensing letters》 - 2025,16(1/3) - 66～76 - 共11页

摘要： sparse component in robust principal component analysis | characters of target images, a robust principal component | analysis combined with top-hat transform (RPCA-THT) is | component with a specially designed kernel matrix. Then | the sparse component* shrinks according to the*

关键词： Ground penetrating radar | clutter suppression | robust principal component analysis | top-hat transform | low-rank sparse decomposition

10. Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation NSTL国家科技图书文献中心

Hostrup, Mads C. F. | Nielsen, Anne Sofie ... - 《Physiological measurement》 - 2025,46(3) - 共10页

摘要：, principal* component analysis (PCA), and autocorrelation* | Objective. Respiratory rate (RR) is an | important vital sign but is often neglected. Multiple | technologies exist for RR monitoring but are either expensive | or impractical. Tri-axial accelerometry represents

摘要： Functional Principal* Component Analysis (SMFPCA), which* | performs principal* component analysis for multivariate* | applies spectral functional component* analysis to* | This paper is devoted to the study of | dimension reduction techniques for multivariate spatially

关键词： Spectral analysis | Functional data analysis | Functional principal component analysis | Spatial-functional principal component | analysis | Multivariate analysis

摘要：Kernel principal* component analysis (kernel* | the extraction of informative principal* components* | learning, enabling effective analysis* of input data* | PCA) is a nonlinear dimensionality reduction | technique that employs kernel functions to map data into a

关键词： quantum computing | machine learning | kernel method | kernel principal component analysis

摘要：Tensor Robust Principal Component Analysis | (TRPCA) aims to recover clean tensor data corrupted | with noise. This method finds significant application | in recovering multidimensional data. However, the | majority of existing methods rely solely on local

关键词： Non-local similarity | Hankel structure | Tensor robust principal component analysis | Tensor nuclear norm | Alternating direction method of multiplier

摘要：, Principal* Component Analysis (PCA) performs both the* | As a widely used method in signal processing | compression and the recovery of high dimensional data by | leveraging the linear transformations. Considering the | robustness of PCA, how to discriminate correct samples and

关键词： Principal component analysis | Minimization | Optimization | Signal processing algorithms | Robustness | High dimensional data | Dimensionality reduction | Lagrangian functions | Iterative algorithms | Adaptation models

摘要： component analysis(PCA)to reduce the key infuential | aims to investigate the ability of principal | -based anode.The result was evaluated using principal | principal factors are intricately interconnected,resulting | Electrocoagulation can enhance struvite

关键词： ammonium | phosphorus | struvite | electrocoagulation | principal component analysis

摘要： component analysis (sFPCA) method to extract image | . We propose a novel supervised functional principal | ABSTRACT Brain imaging data is one of the | primary predictors for assessing the risk of Alzheimer's | disease (AD). This study aims to extract image‐based

关键词： functional principal component analysis | image analysis | survival analysis | triangulation

摘要： Component Analysis (IPCA). The proposed method aims to | Genetic Algorithm (GA) with Incremental Principal | © 2024Medical imaging has an crucial role in | modern healthcare and helps diagnosing and treating for | a variety of medical conditions. However, the

关键词： Genetic Algorithm | Image reconstruction | Incremental Principal Component Analysis | MRI

摘要： determined using principal* component *analysis, and the | As a widely produced renewable, low-cost | biomass resource, corn straw's physicochemical | properties and utilization potential have been extensively | studied in order to utilize it with high value and

关键词： Corn straw | Segmented utilization | Index system | Principal component analysis

摘要： sparse component in robust principal component analysis | characters of target images, a robust principal component | analysis combined with top-hat transform (RPCA-THT) is | component with a specially designed kernel matrix. Then | the sparse component* shrinks according to the*

关键词： Ground penetrating radar | clutter suppression | robust principal component analysis | top-hat transform | low-rank sparse decomposition

摘要：, principal* component analysis (PCA), and autocorrelation* | Objective. Respiratory rate (RR) is an | important vital sign but is often neglected. Multiple | technologies exist for RR monitoring but are either expensive | or impractical. Tri-axial accelerometry represents

关键词： respiratory rate | accelerometer | respiratory measurement | principal component analysis | autocorrelation

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Functional Principal Component Analysis (SMFPCA), which | performs principal component analysis for multivariate | applies spectral functional component analysis to | This paper is devoted to the study of | dimension reduction techniques for multivariate spatially

关键词： Spectral analysis | Functional data analysis | Functional principal component analysis | Spatial-functional principal component | analysis | Multivariate analysis

摘要：Kernel principal component analysis (kernel | the extraction of informative principal components | learning, enabling effective analysis of input data | PCA) is a nonlinear dimensionality reduction | technique that employs kernel functions to map data into a

关键词： quantum computing | machine learning | kernel method | kernel principal component analysis

摘要：Tensor Robust Principal Component Analysis | (TRPCA) aims to recover clean tensor data corrupted | with noise. This method finds significant application | in recovering multidimensional data. However, the | majority of existing methods rely solely on local

关键词： Non-local similarity | Hankel structure | Tensor robust principal component analysis | Tensor nuclear norm | Alternating direction method of multiplier

摘要：, Principal Component Analysis (PCA) performs both the | As a widely used method in signal processing | compression and the recovery of high dimensional data by | leveraging the linear transformations. Considering the | robustness of PCA, how to discriminate correct samples and

关键词： Principal component analysis | Minimization | Optimization | Signal processing algorithms | Robustness | High dimensional data | Dimensionality reduction | Lagrangian functions | Iterative algorithms | Adaptation models

摘要： component analysis(PCA)to reduce the key infuential | aims to investigate the ability of principal | -based anode.The result was evaluated using principal | principal factors are intricately interconnected,resulting | Electrocoagulation can enhance struvite

关键词： ammonium | phosphorus | struvite | electrocoagulation | principal component analysis

摘要： component analysis (sFPCA) method to extract image | . We propose a novel supervised functional principal | ABSTRACT Brain imaging data is one of the | primary predictors for assessing the risk of Alzheimer's | disease (AD). This study aims to extract image‐based

关键词： functional principal component analysis | image analysis | survival analysis | triangulation

摘要： Component Analysis (IPCA). The proposed method aims to | Genetic Algorithm (GA) with Incremental Principal | © 2024Medical imaging has an crucial role in | modern healthcare and helps diagnosing and treating for | a variety of medical conditions. However, the

关键词： Genetic Algorithm | Image reconstruction | Incremental Principal Component Analysis | MRI

摘要： determined using principal component analysis, and the | As a widely produced renewable, low-cost | biomass resource, corn straw's physicochemical | properties and utilization potential have been extensively | studied in order to utilize it with high value and

关键词： Corn straw | Segmented utilization | Index system | Principal component analysis

摘要： sparse component in robust principal component analysis | characters of target images, a robust principal component | analysis combined with top-hat transform (RPCA-THT) is | component with a specially designed kernel matrix. Then | the sparse component shrinks according to the

关键词： Ground penetrating radar | clutter suppression | robust principal component analysis | top-hat transform | low-rank sparse decomposition

摘要：, principal component analysis (PCA), and autocorrelation | Objective. Respiratory rate (RR) is an | important vital sign but is often neglected. Multiple | technologies exist for RR monitoring but are either expensive | or impractical. Tri-axial accelerometry represents

关键词： respiratory rate | accelerometer | respiratory measurement | principal component analysis | autocorrelation

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Functional Principal* Component Analysis (SMFPCA), which* | performs principal* component analysis for multivariate* | applies spectral functional component* analysis to* | This paper is devoted to the study of | dimension reduction techniques for multivariate spatially

摘要：Kernel principal* component analysis (kernel* | the extraction of informative principal* components* | learning, enabling effective analysis* of input data* | PCA) is a nonlinear dimensionality reduction | technique that employs kernel functions to map data into a

摘要：, Principal* Component Analysis (PCA) performs both the* | As a widely used method in signal processing | compression and the recovery of high dimensional data by | leveraging the linear transformations. Considering the | robustness of PCA, how to discriminate correct samples and

摘要： determined using principal* component *analysis, and the | As a widely produced renewable, low-cost | biomass resource, corn straw's physicochemical | properties and utilization potential have been extensively | studied in order to utilize it with high value and

摘要： sparse component in robust principal component analysis | characters of target images, a robust principal component | analysis combined with top-hat transform (RPCA-THT) is | component with a specially designed kernel matrix. Then | the sparse component* shrinks according to the*

摘要：, principal* component analysis (PCA), and autocorrelation* | Objective. Respiratory rate (RR) is an | important vital sign but is often neglected. Multiple | technologies exist for RR monitoring but are either expensive | or impractical. Tri-axial accelerometry represents