Robust Multi-Label Classification with Enhanced Global and Local Label Correlation

Tianna Zhao, Yuanjian Zhang and Witold Pedrycz
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Tianna Zhao: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Yuanjian Zhang: China UnionPay Co., Ltd, Shanghai 201201, China
Witold Pedrycz: Department of Electrical & Computer Engineering, Alberta University, Edmonton, AB T6R 2V4, Canada

Mathematics, 2022, vol. 10, issue 11, 1-23

Abstract: Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons that model robustness degrades. Aiming at the cases with noisy features and missing labels, we develop a novel method called robust global and local label correlation (RGLC). In this model, subspace learning reconstructs intrinsic latent features immune from feature noise. The manifold learning ensures that outputs obtained by matrix factorization are similar in the low-rank latent label if the latent features are similar. We examine the co-occurrence of global and local label correlation with the constructed latent features and the latent labels. Extensive experiments demonstrate that the classification performance with integrated information is statistically superior over a collection of state-of-the-art approaches across numerous domains. Additionally, the proposed model shows promising performance on multi-label when noisy features and missing labels occur, demonstrating the robustness of multi-label classification.

Keywords: multi-label classification; label correlations; noisy features; missing labels; robustness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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