AEROSPACE SHANGHAI, Volume. 42, Issue 1, 149(2025)

A Classification Method for Rocket Weld Seam Radiographic Digital Imaging Based on Robust Principal Component Analysis

Ning WANG*, Xiao LIU, Xiaojia LIU, and Quan WEI
Author Affiliations
  • Shanghai Spaceflight Precision Machinery Institute, Shanghai201699, China
  • show less
    References(28)

    [4] N N PISE, P KULKARNI. A survey of semi-supervised learning methods. IEEE, 30-34(2008).

    [6] I T JOLIFFE. Principle component analysis, 111-126(1986).

    [7] A HYVARINEN. Independent component analysis. Algorithms and Applications Neural Networks, 13, 411-430(2000).

    [9] J B TENENBAUM. Mapping a manifold of perceptual observations. Advances in Neural Information Processing Systems, 21, 682-688(1998).

    [10] S ROWEIS, L SAUL. Nonlinear dimensionality reduction by locally linear embedding. Sciences, 1323-1396(2000).

    [11] M BELKIN, P NIYOGI. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373-1396(2003).

    [12] M BRAND, K HUANG. A unifying theorem for spectral embedding and clustering. AI and Stats, 1-8(2003).

    [13] H HOTELLING. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441(1933).

    [14] B SCHÖLKOPF, A SMOLA, K MÜLLER. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319(2008).

    [15] A OUYANG, Y LIU, S PEI et al. A hybrid improved kernel LDA and PNN algorithm for efficient face recognition. Neurocomputing, 393, 214-222(2019).

    [16] M REZAEI-RAVARI, M EFTEKHARI, F S MOVAHED. Regularizing extreme learning machine bydual locally linear embedding manifold learning for training multi-label neural network classifiers. Engineering Applications of Artificial Intelligence, 97, 1-15(2020).

    [17] M BELKIN, P NIYOGI. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373-1396(2003).

    [18] X YE, H LI, A IMAKURA et al. An oversampling framework for imbalanced classification based on Laplacian eigenmaps. Neurocomputing, 399, 107-116(2020).

    [19] E J CANDÈS, X D LI, M A YL et al. Robust principal component analysis. Journal of the ACM (JACM), 58, 1-11(2011).

    [20] Z ZHANG, M ZHAO, F LI et al. Robust alternating low-rank representation by joint Lp-and L2,p-norm minimization. Neural Networks, 96, 55-70(2017).

    [21] F R LI. Variable selection via nonconcave penalized likelihood and its oracle properties. Publications of the American Statal Association, 96, 1348-1360(2001).

    [22] C GAO, N WANG, Q YU et al. A feasible nonconvex relaxation approach to feature selection, 356-361(2014).

    [23] J WANG, F ZHANG, J HUANG et al. A nonconvex penalty function with integral convolution approximation for compressed sensing. Signal Processing, 158, 116-128(2019).

    [24] Y LI, G LIU, Q LIU et al. Moving object detection via segmentation and saliency constrained RPCA. Neurocomputing, 323, 352-362(2019).

    [25] M MOHADDESEH, M AMINOLLAH, R AKBAR. RPCA-based real-time speech and music separation method-ScienceDirect. Speech Communication, 126, 22-34(2021).

    [26] Y FU, Y WANG, Y ZHONG et al. Change detection based on tensor RPCA for longitudinal reitnal fundus images. Neurocomputing, 387, 1-12(2020).

    [27] F NIE, X WANG, M I JORDAN et al. The constrained laplacian rank algorithm for graph-based clustering, 1969-1976(2016).

    [28] Z H ZHOU. A brief introduction to weakly supervised learning. National Science Review, 1-10(2017).

    [30] J F CAI, J EMMANUEL, Z SHEN et al. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20, 1956-1982(2010).

    [31] I DAUBECHIES, M DEFRISE, M C DE. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57, 1413-1457(2010).

    [33] J A COSTA, A O H III. Classification constrained dimensionality reduction, 1-10(2005).

    [34] K KIM, J LEE. Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recognition, 47, 758-768(2014).

    Tools

    Get Citation

    Copy Citation Text

    Ning WANG, Xiao LIU, Xiaojia LIU, Quan WEI. A Classification Method for Rocket Weld Seam Radiographic Digital Imaging Based on Robust Principal Component Analysis[J]. AEROSPACE SHANGHAI, 2025, 42(1): 149

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Integration of Material Structure and Function

    Received: Aug. 3, 2024

    Accepted: --

    Published Online: May. 13, 2025

    The Author Email:

    DOI:10.19328/j.cnki.2096-8655.2025.01.016

    Topics