Balanced Self-Paced Learning for AUC Maximization Article Swipe
Bin Gu
,
Chenkang Zhang
,
Huan Xiong
,
Heng Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.03650
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.03650
Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.03650
- https://arxiv.org/pdf/2207.03650
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285070146
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285070146Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2207.03650Digital Object Identifier
- Title
-
Balanced Self-Paced Learning for AUC MaximizationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-08Full publication date if available
- Authors
-
Bin Gu, Chenkang Zhang, Huan Xiong, Heng HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.03650Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.03650Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2207.03650Direct OA link when available
- Concepts
-
Maximization, Computer science, Artificial intelligence, Mathematical optimization, Generalization, Stationary point, Pointwise, Machine learning, Mathematics, Algorithm, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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