MSAFC: Matrix subspace analysis with fuzzy clustering ability

Jun Gao, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In this paper, based on the maximum margin criterion (MMC) together with the fuzzy clustering and the tensor theory, a novel matrix based fuzzy maximum margin criterion (MFMMC) is proposed and based upon which a matrix subspace analysis method with fuzzy clustering ability (MSAFC) is derived. Besides, according to the intuitive geometry, a proper method of setting the adjustable parameter γ in the proposed criterion MFMMC is given and its rationale is provided. The proposed method MSAFC can simultaneously realize unsupervised feature extraction and fuzzy clustering for matrix data (e.g. image data). As to the running efficiency of MSAFC, a two-directional orthogonal method of dealing with matrix data without any iteration is developed to improve it. Experimental results on UCI datasets, hand-written digit datasets, face image datasets and gene datasets show the distinctive performance of MSAFC.
Original languageEnglish
Pages (from-to)1143-1163
Number of pages21
JournalSoft Computing
Issue number6
Publication statusPublished - 1 Jan 2014


  • Fuzzy clustering
  • Matrix based fuzzy maximum margin criterion
  • Matrix subspace analysis
  • Two-directional 2D feature extraction

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology


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