2025年 03期

摘要(Abstract)：

为了解决实际应用中多视图数据经常包含缺失或异常信息，以及现有不完全多视图聚类方法在优化数据相似性矩阵时未能充分表示原始数据相似性，增加计算复杂性并忽视视图间判别信息差异等问题，提出一种基于余弦相似自适应加权视图重构的不完全多视图聚类算法，通过引入局部保留重建项以实现对缺失视图的自然对齐，避免使用平均值填充缺失视图可能带来的负面影响。在初始化阶段，算法通过计算原始多视图空间中的余弦相似度增强原始多视图数据的流形结构保持能力，并在构建完整视图过程中采用自适应加权策略捕捉不同视图的重要性。在4个基准数据集中进行聚类实验，并与现有9种代表性算法的最优结果相比较。结果表明，所提算法的聚类精度、归一化互信息率和纯度的平均值分别提升了5.52%、 8.78%和4.77%,具有出色的不完全多视图聚类性能。

关键词(KeyWords)：不完全多视图聚类;自适应加权;余弦相似性;流形结构;视图重构

基金项目(Foundation):国家自然科学基金项目(72471123,62172229);; 江苏省基础科学研究项目(24KJA630001)

作者(Author): 陈永泰,邱野,万鸣华,杨国为

DOI: 10.13349/j.cnki.jdxbn.20250313.002

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