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Model-based sparse coding beyond Gaussian independent model

Author

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  • Xing, Xin
  • Xie, Rui
  • Zhong, Wenxuan

Abstract

Sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority of related studies are restricted to continuous data without spatial or temporal structure. A new model-based sparse coding (MSC) method is proposed to provide an effective and flexible framework for learning features from different data types: continuous, discrete, or categorical, and modeling different types of correlations: spatial or temporal. The specification of the sparsity level and how to adapt the estimation method to large-scale studies are also addressed. A fast EM algorithm is proposed for estimation, and its superior performance is demonstrated in simulation and multiple real applications such as image denoising, brain connectivity study, and spatial transcriptomic imaging.

Suggested Citation

  • Xing, Xin & Xie, Rui & Zhong, Wenxuan, 2022. "Model-based sparse coding beyond Gaussian independent model," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:csdana:v:166:y:2022:i:c:s0167947321001705
    DOI: 10.1016/j.csda.2021.107336
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    References listed on IDEAS

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    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Ma, Ping & Zhong, Wenxuan, 2008. "Penalized Clustering of Large-Scale Functional Data With Multiple Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 625-636, June.
    3. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
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