提出三种掩码聚类方法，显著提升无监督子空间聚类性能。
Masked Subspace Clustering Methods
- 基于双层优化框架，设计硬掩码与软掩码机制。
- 在MNIST、USPS等5个数据集上优于基准方法。
- 可学习软掩码，适合需要自适应特征选择的场景。
为更充分地利用无监督特征和成对信息，本文提出通用的双层聚类优化（BCO）框架以提升聚类性能。随后引入三种子空间聚类的特例，采用两类不同掩码：首先将原始子空间聚类重构为基本掩码子空间聚类（BMSC），将对角约束改为硬掩码；其次提出通用掩码子空间聚类（GMSC），通过软掩码整合多种聚类结果。进一步地，基于BCO与GMSC，设计可学习的软掩码，提出递归掩码子空间聚类（RMSC）方法，可交替更新相似度矩阵与软掩码。数值实验表明，所提模型在多个常用数据集（如MNIST、USPS、ORL、COIL20、COIL100）上相较基线有显著提升。
原文摘要 · Abstract (English)
To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering. And then we introduce three special cases on subspace clustering with two different types of masks. At first, we reformulate the original subspace clustering as a Basic Masked Subspace Clustering (BMSC), which reformulate the diagonal constraints to a hard mask. Then, we provide a General Masked Subspace Clustering (GMSC) method to integrate different clustering via a soft mask. Furthermore, based on BCO and GMSC, we induce a learnable soft mask and design a Recursive Masked Subspace Clustering (RMSC) method that can alternately update the affinity matrix and the soft mask. Numerical experiments show that our models obtain significant improvement compared with the baselines on several commonly used datasets, such as MNIST, USPS, ORL, COIL20 and COIL100.
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