用AI理解用户偏好，自动定制数据聚类方式。
Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
- 通过CLIP与GPT-4生成文本代理词，构建用户特定子空间
- 在多个图像数据集上超越现有方法，聚类结果更贴合用户需求
- 适合需要个性化聚类的科研与工业场景
多聚类旨在从不同角度发现数据的潜在结构。深度多聚类方法通过挖掘数据中的复杂模式和关系取得了显著进展。然而，现有方法难以灵活适应用户特定的数据分组需求，往往需要人工理解每个聚类结果。为此，本文提出Multi-Sub，一种端到端的多聚类方法，引入多模态子空间代理学习框架。利用CLIP与GPT-4的协同能力，将表达用户偏好的文本提示与其对应的视觉表示对齐。通过大语言模型自动生成代理词作为子空间基底，实现基于用户兴趣的数据表征定制化。该方法在多个视觉多聚类任务中持续优于现有基线。代码已开源：https://github.com/Alexander-Yao/Multi-Sub。
原文摘要 · Abstract (English)
Multiple clustering aims to discover various latent structures of data from different aspects. Deep multiple clustering methods have achieved remarkable performance by exploiting complex patterns and relationships in data. However, existing works struggle to flexibly adapt to diverse user-specific needs in data grouping, which may require manual understanding of each clustering. To address these limitations, we introduce Multi-Sub, a novel end-to-end multiple clustering approach that incorporates a multi-modal subspace proxy learning framework in this work. Utilizing the synergistic capabilities of CLIP and GPT-4, Multi-Sub aligns textual prompts expressing user preferences with their corresponding visual representations. This is achieved by automatically generating proxy words from large language models that act as subspace bases, thus allowing for the customized representation of data in terms specific to the user's interests. Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks. Our code is available at https://github.com/Alexander-Yao/Multi-Sub.
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