Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34991
 Title: MSAFC : matrix subspace analysis with fuzzy clustering ability Authors: Gao, JChung, F Wang, S Keywords: Matrix subspace analysisTwo-directional 2D feature extractionMatrix based fuzzy maximum margin criterionFuzzy clustering Issue Date: 2014 Publisher: Springer Source: Soft computing, 2014, v. 18, no. 6, p. 1143-1163 How to cite? Journal: Soft computing Abstract: 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 $$\gamma$$ 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. URI: http://hdl.handle.net/10397/34991 ISSN: 1432-7643 DOI: 10.1007/s00500-013-1134-3 Appears in Collections: Journal/Magazine Article

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