Scalable multi-view clustering via explicit kernel features maps

Chakib Fettal

doi:10.1007/s10618-026-01187-x

Scalable multi-view clustering via explicit kernel features maps

2026-03-16

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DOI：10.1007/s10618-026-01187-x

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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.

Keywords:

Multi-view clustering

Community detection

Graph clustering

Subspace clustering

Scalable multi-view clustering via explicit kernel features maps

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