Abstract
Analysis dictionary learning (ADL) model has attracted much interest from researchers in representation-based classification due to its scalability and efficiency in out-of-sample classification. However, the discrimination of the analysis representation is not fully explored when roughly consider the supervised information with redundant and noisy samples. In this paper, we propose a discriminative and robust analysis dictionary learning model (DR-ADL), which explores the underlying structural information of data samples. Firstly, the supervised latent structural term is first implicitly considered to generate a roughly block-diagonal representation for intra-class samples. However, this discriminative structure is fragile and weak in the presence of noisy and redundant samples. Concentrating on both intra-class and inter-class information, we then explicitly incorporate an off-block suppressing term on the ADL model for discriminative structure representation. Moreover, non-negative constraint is incorporated on representations to ensure a reasoning explanation for the contributions of each atoms. Finally, the DR-ADL model is alternatively solved by the K-SVD method, iterative re-weighted method and gradient method efficiently. Experimental results on four benchmark face datasets classification validate the performance superiority of our DR-ADL model.
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This work is supported by the Natural Science Basic Research Program of Shaanxi, China (Program No. 2021JM-339).
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Jiang, K., Zhu, L., Liu, Z. (2022). Discriminative and Robust Analysis Dictionary Learning for Pattern Classification. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_31
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