Abstract
Analyzing large-scale datasets, especially involving complex and high-dimensional data like images, is particularly challenging. While self-supervised learning (SSL) has proven effective for learning representations from unlabelled data, it typically focuses on flat, non-hierarchical structures, missing the multi-level relationships present in many real-world datasets. Hierarchical clustering (HC) can uncover these relationships by organizing data into a tree-like structure, but it often relies on rigid similarity metrics that struggle to capture the complexity of diverse data types. To address these we envision $\texttt{InfoHier}$, a framework that combines SSL with HC to jointly learn robust latent representations and hierarchical structures. This approach leverages SSL to provide adaptive representations, enhancing HC's ability to capture complex patterns. Simultaneously, it integrates HC loss to refine SSL training, resulting in representations that are more attuned to the underlying information hierarchy. $\texttt{InfoHier}$ has the potential to improve the expressiveness and performance of both clustering and representation learning, offering significant benefits for data analysis, management, and information retrieval.
Abstract (translated)
分析大规模数据集,尤其是涉及如图像这般复杂且高维度的数据时,是一项极具挑战性的任务。尽管自监督学习(SSL)在从无标签数据中学习表示方面表现出了有效性,但它通常侧重于扁平、非层次化的结构,忽略了实际数据集中存在的多层次关系。分层聚类(HC)能够通过将数据组织成树状结构来揭示这些关系,但其往往依赖于刚性的相似性度量标准,难以捕捉不同类型复杂数据的本质特征。 为了解决这些问题,我们提出了一种新的框架$\texttt{InfoHier}$,它结合了SSL和HC,以共同学习出稳健的潜在表示与层级结构。该方法利用SSL提供自适应表示能力,从而增强HC捕捉复杂模式的能力;同时,它还整合了HC损失函数来优化SSL训练过程,生成更加符合底层信息层次的表征。 $\texttt{InfoHier}$有望提升聚类和表示学习两方面的表现力与性能,在数据分析、管理和信息检索方面带来显著优势。
URL
https://arxiv.org/abs/2501.08717