摘要：

多视图聚类重点挖掘不同视图间的一致性信息, 以提升多视图聚类性能。大多数现有的多视图聚类算法主要针对单任务多视图聚类, 忽略了相关任务的相似性, 使得多任务多视图聚类性能较差。而多任务聚类能够有效处理多个任务的相关性, 实际中普遍存在的聚类问题是多任务多视图数据聚类。为了更好地探究相关任务的关联性, 同时从每个任务的多视图数据中获得更有效的一致性信息, 提出一种基于共识图学习的多任务多视图聚类算法。该算法建立特定于视图的共享特征库, 存储并迁移所有任务和所有视图共享的潜在信息, 即每个任务在公共视图下共享的特征嵌入信息。当处理新任务时, 一方面新任务的每个视图先通过同时优化相似图结构与对应的样本嵌入, 以获得更准确的样本嵌入表示, 另一方面引入协同聚类, 实现共享特征库与新任务样本嵌入的知识迁移。这种方式利用特征嵌入的多样性信息来促进新任务各视图的一致性表达, 同时根据新任务的样本信息更新共享特征库。在获得最优的样本嵌入表示后, 将所有视图融合在一起, 学习新任务的一个共识图。随后, 采用交替方向策略优化模型, 最终从共识图的拉普拉斯矩阵中引入秩约束以直接获得聚类结果。实验结果表明, 与现有的6种先进算法相比, 该算法在5个多任务多视图数据集上表现出更高的聚类性能和效率。

关键词: 多视图聚类, 多任务学习, 共识表示, 图学习, 协同聚类

Abstract:

Multi-view clustering focuses on mining consistency information between different views to improve performance. Most existing multi-view clustering algorithms focus on single-task multi-view clustering while ignoring the similarity of related tasks, which results in poor performance on multiple tasks. Multi-task clustering can effectively handle the correlation between multiple tasks, and the most common clustering problem in practice is multi-task multi-view data clustering. To better explore the correlation between related tasks and obtain more effective consistency information from the multi-view data of each task, this paper proposes a multi-task multi-view clustering algorithm based on consensus graph learning. This algorithm establishes a view-specific shared feature library, which stores and migrates all tasks and potential information shared by all views, that is, the feature-embedding information shared by each task in the common view. When dealing with new tasks, each view of a new task optimizes the similar graph structure and corresponding sample embeddings simultaneously to obtain more accurate sample embedding representations. Meanwhile, collaborative clustering is introduced to achieve knowledge transfer between shared feature libraries and new task sample embeddings. This approach utilizes the diversity information of feature embedding to promote the consistent expression of various views in the new task, while updating the shared feature library based on the sample information of this new task. After obtaining the optimal sample-embedding representation, all views are fused to obtain a consensus graph for the new task. Subsequently, an alternating direction strategy is adopted to optimize the model, and rank constraints from the Laplacian matrix of the consensus graph are introduced to directly obtain the clustering results. The results of experiments show that, compared with six existing advanced algorithms, the proposed algorithm exhibits higher clustering performance and efficiency on five multi-task multi-view datasets.

Key words: multi-view clustering, multi-task learning, consensus representation, graph learning, co-clustering