Model-based sparse coding beyond Gaussian independent model
نویسندگان
چکیده
Sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority related studies are restricted continuous without spatial or temporal structure. A new model-based (MSC) method is proposed provide an effective and flexible framework for learning features from different types: continuous, discrete, categorical, modeling types correlations: temporal. The specification the sparsity level how adapt estimation large-scale also addressed. fast EM algorithm estimation, its superior performance demonstrated in simulation multiple real applications such image denoising, brain connectivity study, transcriptomic imaging.
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2022
ISSN: ['0167-9473', '1872-7352']
DOI: https://doi.org/10.1016/j.csda.2021.107336