S3DL: Sample-Aggregated Structured Supervised Dictionary Learning

doi:10.1109/LSP.2026.3704064

S3DL: Sample-Aggregated Structured Supervised Dictionary Learning

2026-06-16

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DOI：10.1109/LSP.2026.3704064

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This paper proposes Sample-aggregated Structured Supervised Dictionary Learning (S3DL), a novel framework for robust classification in noisy and outlier-prone scenarios. S3DL jointly optimizes synthesis and analysis dictionaries, along with a meta-sample aggregation projection and adaptive weighting matrices, to learn a discriminative latent feature space that suppresses noise and outliers while enhancing classification. The resulting constrained multi-objective optimization problem is efficiently solved via a tailored alternating optimization algorithm. Furthermore, an error bound is derived to guarantee its robustness and efficiency. Extensive experiments on seven benchmarks demonstrate that S3DL consistently outperforms state-of-the-art dictionary learning methods in classification accuracy.

Keywords:

Dictionary pair learning

sample aggregation

constrained multi-objective optimization

S<sup>3</sup>DL: Sample-Aggregated Structured Supervised Dictionary Learning

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