Semi-Supervised Spectral Clustering with Structured Sparsity Regularization

Research output: Research - peer-review21_Publication in refereed journal

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Original languageEnglish
Article number8253493
Pages (from-to)403-407
Journal / PublicationIEEE Signal Processing Letters
Issue number3
Early online date10 Jan 2018
StatePublished - Mar 2018


Spectral clustering (SC) is one of the most widely used clustering methods. In this letter, we extend the traditional SC with a semi-supervised manner. Specifically, with the guidance of small amount of supervisory information, we build a matrix with anti-block-diagonal appearance, which is further utilized to regularize the product of the low-dimensional embedding and its transpose. Technically, we formulate the proposed model as a constrained optimization problem. Then, we relax it as a convex problem, which can be efficiently solved with the global convergence guaranteed via the inexact augmented Lagrangian multiplier method. Experimental results over four real-world datasets demonstrate that higher accuracy and normalized mutual information are achieved when compared with state-of-the-art methods.

Research Area(s)

  • Convex optimization, semi-supervised, spectral clustering (SC)