通过聚类结果引导，提升缺失视图数据的聚类性能。
Clustering Result Re-guided Incomplete Multi-view Spectral Clustering
- 用非负约束直接从特征中生成聚类结果。
- 基于聚类连通性构建矩阵，优化自表示残差。
- 适合处理多视图数据中部分缺失的场景。
不完整多视图谱聚类将谱聚类推广到多视图数据，并在存在缺失视图的情况下实现数据划分。传统方法需在特征提取后使用K-means生成聚类结果，且未充分利用聚类结果反映的样本连通性。为此，本文提出聚类结果重引导的不完整多视图谱聚类（CRG_IMSC）。CRG_IMSC通过施加非负约束直接从提取的特征中获得聚类结果；同时根据谱聚类结果构建连通性矩阵，并基于该矩阵最小化自表示残差。设计了一种基于乘法更新的新型迭代算法求解优化问题，并严格证明了其收敛性。在基准数据集上的实验表明，对于多视图数据，CRG_IMSC优于现有先进聚类方法，且算法收敛性得到验证。
原文摘要 · Abstract (English)
Incomplete multi-view spectral clustering generalizes spectral clustering to multi-view data and simultaneously realizes the partition of multi-view data with missing views. For this category of method, K-means algorithm needs to be performed to generate the clustering result after the procedure of feature extraction. More importantly, the connectivity of samples reflected by the clustering result is not utilized effectively. To overcome these defects, we propose Clustering Result re-Guided Incomplete Multi-view Spectral Clustering (CRG_IMSC). CRG_IMSC obtains the clustering result directly by imposing nonnegative constraint to the extracted feature. Furthermore, it constructs the connectivity matrix according to the result of spectral clustering, and minimizes the residual of self-representation based on the connectivity matrix. A novel iterative algorithm using multiplicative update is developed to solve the optimization problem of CRG_IMSC, and its convergence is proved rigorously. On benchmark datasets, for multi-view data, CRG_IMSC performs better than state-of-the-art clustering methods, and the experimental results also demonstrate the convergence of CRG_IMSC algorithm.
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