Scalable Multi-view Clustering via Explicit Kernel Features Maps

2024/02/07 by Chakib Fettal, Fettal, Chakib, Lazhar Labiod +3
Computer Science · #Advanced Clustering Algorithms Research #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2402.04794

openalex publication_date 2024/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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