RMFRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
نویسندگان
چکیده
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with adaptive structure learning (RMFRASL), which can select discriminative features from large amount of multimedia data to improve the performance classification and clustering tasks. RMFRASL integrates three models (robust factorization, learning, regularization) into unified framework. More specifically, factorization-based (RMFFS) model is proposed by introducing an indicator measure importance features, L21-norm adopted as metric enhance robustness selection. Furthermore, (RASL) based on self-representation capability samples designed discover geometric relationships original data. Lastly, regularization (SR) term learned graph structure, constrains selected preserve information in space. To solve objective function our RMFRASL, iterative optimization algorithm proposed. By comparing some state-of-the-art approaches several publicly available databases, advantage demonstrated.
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ژورنال
عنوان ژورنال: Algorithms
سال: 2022
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a16010014