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Online kernel-based clustering
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DOI：10.1016/j.patcog.2024.111009.png)
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Abstract
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A novel online joint kernel learning and clustering (OKC) framework is derived which is capable of determining time-varying clustering configurations without the need for training data. To facilitate clustering via sparse kernel factorization, a novel time-dependent metric is devised to quantify closeness of candidate kernel similarity matrices to a block diagonal structure. The task is later performed online as newly acquired data are processed making it appropriate for non-stationary settings. The necessary minimization to carry out kernel learning and clustering is performed via an effective interplay among block coordinate descent, difference of convex functions minimization and projected subgradient descent which results in an online iterative algorithm that updates online the kernel covariance matrix, as well as the associated clustering configuration. The resulting recursive kernel updates are proved to converge to a bounded limit. Detailed numerical examples utilizing both synthetic and real data show the superior clustering accuracy achieved by the novel approach over existing alternatives while requiring considerably less running time.
Keywords:
Kernel learning
Online algorithms
Clustering
Optimization
Journal
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7.6
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1.3W
Citations:
4.5W
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