Semi-Supervised Spectral Clustering with Structured Sparsity Regularization

Yuheng JIA, Sam KWONG, Junhui HOU

Research output: Journal PublicationsJournal Article (refereed)peer-review

33 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)403-407
JournalIEEE Signal Processing Letters
Volume25
Issue number3
Early online date10 Jan 2018
DOIs
Publication statusPublished - Mar 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China under Grant 61672443 and in part by Hong Kong RGC General Research Funds 9042489 (CityU 11206317) and 9042322 (CityU 11200116).

Keywords

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

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