Unified One-Step Multi-View Spectral Clustering
Unified One-Step Multi-View Spectral Clustering
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DOI：
10.1109/tkde.2022.3172687
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发表时间：
2023-06
影响因子：
8.9
通讯作者：
Chang Tang;Zhenglai Li;J. Wang;Xinwang Liu;Wei Zhang;En Zhu
中科院分区：
文献类型：
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作者：
Chang Tang;Zhenglai Li;J. Wang;Xinwang Liu;Wei Zhang;En Zhu
多视图谱聚类利用不同视图图之间的互补信息来获得优异的聚类结果，最近引起了人们的广泛关注。然而，大多数现有的多视图谱聚类方法通过两步方案获得聚类分区，即谱嵌入和随后的$k$k-means。由于两步过程中的信息丢失，这种两步方案不可避免地会寻求次优的聚类结果。此外，现有的多视图谱聚类方法没有共同利用图和嵌入矩阵的信息，这也降低了最终的聚类结果。为了解决这些问题，我们提出了一种统一的一步多视图谱聚类方法，它将谱嵌入和$k$k-means集成到一个统一的框架中，以一步策略获得离散的聚类标签。在观察到嵌入矩阵的内积是图的低秩近似的情况下，我们将不同视图的图和嵌入矩阵组合起来以获得统一的图。然后，我们直接从统一图中捕获离散聚类指标矩阵。此外，我们设计了一种有效的优化算法来解决由此产生的问题。最后，在各种数据集上进行了一组实验来验证所提出方法的有效性。本作品的演示代码公开于 rgb]0,0,1 https://github.com/guanyuezhen/UOMvSC。
Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent $k$k-means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and $k$k-means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available at rgb]0,0,1 https://github.com/guanyuezhen/UOMvSC.