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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Mining

    Background:

    • Unsupervised learning is powerful for clustering but struggles with large, high-dimensional data due to computational costs.
    • Anchor-based methods offer improvements but face challenges in balancing performance and efficiency.
    • Labeled data is often scarce in real-world scenarios, necessitating effective unsupervised or self-supervised approaches.

    Purpose of the Study:

    • To develop a fast self-supervised clustering method that addresses the computational challenges of large-scale and high-dimensional data.
    • To improve the efficiency and performance of clustering algorithms by integrating anchor-based theories and bipartite graph concepts.
    • To propose a novel framework that accelerates semisupervised learning by inferring labels from constructed bipartite graphs.

    Main Methods:

    • A fast semisupervised framework (FSSF) was developed, integrating a balanced K-means-based hierarchical K-means (BKHK) method with bipartite graph theory.
    • A self-supervised clustering approach was proposed, inferring labels from a bipartite graph with k connected components.
    • The method involves obtaining anchor sets via BKHK, constructing a bipartite graph, solving a self-supervised problem using FSSF, and performing label propagation.

    Main Results:

    • The proposed method significantly accelerates general semisupervised learning through the use of anchors.
    • Experimental results on toy and benchmark datasets demonstrate superior performance compared to existing approaches.
    • The integration of BKHK and bipartite graph theory effectively handles large-scale and high-dimensional clustering problems.

    Conclusions:

    • The developed fast self-supervised clustering method offers an efficient and high-performing solution for large-scale and high-dimensional data.
    • The novel framework effectively overcomes the limitations of traditional unsupervised and anchor-based clustering techniques.
    • This approach provides a valuable advancement in unsupervised learning, particularly for scenarios with limited labeled data.