@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2026AdSpR..78..527F,
       author = {{Feng}, Yong and {Chang}, Guobin and {Qian}, Nijia and {Huan}, Yueyang and {Cao}, Yu and {Sun}, Yinxiao},
        title = "{Probabilistic principal component analysis of time variable gravity data considering measurement errors' covariance matrix}",
      journal = {Advances in Space Research},
     keywords = {GRACE, Principal component analysis, Covariance matrix, EM algorithm},
         year = 2026,
        month = jul,
       volume = {78},
       number = {2},
        pages = {527-546},
     abstract = "{Time-variable gravity data are often analyzed with non-parametric
        decompositions such as principal component analysis. However,
        for upstream products such as GRACE Level-2 solutions, the
        observations can contain heterogeneous and correlated
        measurement errors, whose correlations are reflected by a non-
        diagonal error covariance matrix. Fully accounting for this
        covariance information poses challenges to conventional PCA-type
        processing. To address this issue, we propose a new
        probabilistic PCA method that explicitly incorporates the
        measurement-error covariance structure. The proposed model
        decomposes the data into three parts: signals, noises, and
        measurement errors. The signal term is further represented by a
        small number of components that are orthogonal in both the
        temporal and spatial domains, consistent with the
        interpretability of many PCA-based approaches. The method is
        formulated in a measurement-equation-based probabilistic
        framework, and model parameters are estimated optimally in the
        maximum-likelihood sense. An efficient expectation--maximization
        (EM) algorithm is developed for iterative parameter estimation,
        and a suboptimal variant is also tested experimentally. Using
        GRACE Level-2 data as an example, the proposed approach
        successfully suppresses stripe errors for signal reconstruction
        and provides a compact set of orthogonal components for
        spatiotemporal signal analysis. Experimental results demonstrate
        that the proposed methods perform well in both signal
        reconstruction and spatiotemporal characterization.}",
          doi = {10.1016/j.asr.2026.04.008},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026AdSpR..78..527F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}