用自监督对比学习处理带缺失数据的子空间聚类，效果更好更稳定。
Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learning
- 通过掩码生成数据的多个视图，用对比学习训练网络提取不变特征
- 在6个基准数据集上均优于传统与深度学习方法，对缺失数据鲁棒性强
- 适合处理含缺失值的大规模数据，如图像、遥感、推荐系统场景
子空间聚类旨在将位于低维子空间并集中的数据点分组，广泛应用于计算机视觉、高光谱成像和推荐系统。然而，现有方法大多假设数据完整，限制了其在真实世界缺失数据场景下的应用。本文提出一种基于对比自监督的学习框架——对比子空间聚类（CSC），用于处理不完整数据。CSC通过对部分观测输入生成掩码视图，使用类似SimCLR的对比损失训练深度神经网络，学习数据的不变嵌入表示，并采用稀疏子空间聚类进行最终分组。在六个基准数据集上的实验表明，CSC consistently 超越经典及深度学习基线方法，展现出对缺失数据的强大鲁棒性以及对大规模数据集的可扩展性。
原文摘要 · Abstract (English)
Subspace clustering aims to group data points that lie in a union of low-dimensional subspaces and finds wide application in computer vision, hyperspectral imaging, and recommendation systems. However, most existing methods assume fully observed data, limiting their effectiveness in real-world scenarios with missing entries. In this paper, we propose a contrastive self-supervised framework, Contrastive Subspace Clustering (CSC), designed for clustering incomplete data. CSC generates masked views of partially observed inputs and trains a deep neural network using a SimCLR-style contrastive loss to learn invariant embeddings. These embeddings are then clustered using sparse subspace clustering. Experiments on six benchmark datasets show that CSC consistently outperforms both classical and deep learning baselines, demonstrating strong robustness to missing data and scalability to large datasets.
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