The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view clustering, which integrates complementary information from multiple data perspectives, has emerged as a powerful solution. However, existing methods often struggle with scalability and efficiency, particularly on large attributed networks. In this work, we address these limitations by leveraging explicit kernel feature maps and a non-iterative optimization strategy, enabling efficient and accurate clustering on datasets with millions of points.

翻译：随着社交媒体、传感器网络和在线平台等来源的高维数据激增，聚类算法面临新的挑战。多视图聚类通过整合来自多个数据视角的互补信息，已成为一种有效的解决方案。然而，现有方法通常在可扩展性和效率方面存在不足，尤其是在大规模属性网络上。本研究通过利用显式核特征映射和非迭代优化策略，解决了这些局限性，实现了对百万级数据点的高效准确聚类。