Scalable Multi-view Clustering via Explicit Kernel Features Maps

Chakib Fettal; Lazhar Labiod; Mohamed Nadif

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arXiv · 2402.04794

Scalable Multi-view Clustering via Explicit Kernel Features Maps

Chakib Fettal·Lazhar Labiod·Mohamed Nadif

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Abstract

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.

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Chakib Fettal, Lazhar Labiod, Mohamed Nadif. 2024-02-07. Scalable Multi-view Clustering via Explicit Kernel Features Maps. https://arxiv.org/abs/2402.04794

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