One-Shot Distributed Algorithm for PCA With RBF Kernels

He, Fan; Lv, Kexin; Yang, Jie; Huang, Xiaolin

doi:10.1109/lsp.2021.3095017

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the proposed algorithm, a comprehensive test has been conducted. The results of the proposed method with six energy-based algorithms, such as RBF kernel, 24 Krawtchouk, 25 TextureRBF, 26 L2S, 22 ABC-level set, 27 and customized RBF 28 and using four datasets Brodatz, 29 FABRIC 30 artificial images and real images has been compared. It should be noted that these datasets have a total of 120 images.…”

Section: Resultsmentioning

confidence: 99%

Local energy-based image region segmentation using Legendre polynomials

Mahmoudian

Niazi

2023

J. Electron. Imag.

View full text Add to dashboard Cite

.Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Local energy-based image region segmentation using Legendre polynomials

Mahmoudian

Niazi

2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…When selecting the optimal features of the data, the PCA algorithm is used to calculate the relevant employment data in order to reduce the data dimensionality and thus select the best features [17][18][19]. In stage 1 of the process, the PCA algorithm is mainly used to calculate and analyze the covariance of the employment data features.…”

Section: Dimensionality Reduction Operation Methods For Feature Selec...mentioning

confidence: 99%

Research on Expanding Grassroots Employment Space for College Students in the Context of Rural Revitalization + Data Platform

Wei

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In this paper, in the context of rural revitalization + data platform, a grassroots employment platform for college students is designed according to the data architecture, and the data is processed hierarchically to achieve the organization and knowledge accumulation of cross-domain employment data, which makes multiple employment data standardized. Using data analysis parallelization algorithm and dimensionality reduction operation method, the training data are read and loaded into the distributed memory of the employment data platform to select the best employment features. According to the design platform of this paper, the employment rate for those employed in the eastern region and 80.16% in the central region has reached 91.15%. This paper's design model can improve employee efficiency and expand the grassroots employment space of college students, as indicated.

show abstract

Mining Alzheimer’s disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes

Han,

Peng,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionBecause Alzheimer’s disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.MethodsThis study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.ResultsWe leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.DiscussionThe representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.

show abstract

One-Shot Distributed Algorithm for PCA With RBF Kernels

Cited by 3 publications

References 21 publications

Local energy-based image region segmentation using Legendre polynomials

Local energy-based image region segmentation using Legendre polynomials

Research on Expanding Grassroots Employment Space for College Students in the Context of Rural Revitalization + Data Platform

Mining Alzheimer’s disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes

Contact Info

Product

Resources

About