自动化学报2012，Vol.38Issue(4)：549-562,14.DOI:10.3724/SP.J.1004.2012.00549
具有模糊聚类功能的双向二维无监督特征提取方法
(2D)2UFFCA: Two-directional Two-dimensional Unsupervised Feature Extraction Method with Fuzzy Clustering Ability
摘要
Abstract
In this paper, based on the principles of the maximum margin criterion (MMC) and by introducing the fuzzy method and the tensor theory into it, a novel matrix model fuzzy maximum margin criterion (MFMMC) is proposed. Also, on the basis of it, a two-directional two-dimensional unsupervised feature extraction method with fuzzy clustering ability ((2D)2UFFCA) is constructed. This method can directly realize fuzzy clustering of matrix model data. And it can also achieve the two-directional two-dimensional feature extraction of them, that is, the realization of dimension reduction. At the same time, the adjusting parameter γ in the matrix model fuzzy maximum margin criterion is defined reasonably from the respect of geometry intuition, which is proved theoretically. In order to improve the efficiency of feature extraction, an effective method which can find out the projection matrices of matrix model data is presented. The results of tests show the above advantages of the method.关键词
张量模式/双向二维特征提取/矩阵模式的模糊最大间距判别准则/模糊聚类Key words
Tensor model, two-directional two-dimensional feature extraction, matrix model fuzzy maximum margin criterion (MFMMC), fuzzy clustering引用本文复制引用
皋军,孙长银,王士同..具有模糊聚类功能的双向二维无监督特征提取方法[J].自动化学报,2012,38(4):549-562,14.基金项目
国家自然科学基金(90820002,60903100,61005008),江苏省自然科学基金(BK2011417),江苏省新型环保重点实验室开放课题(AE201068),江苏省计算机信息处理重点实验室开放课题(KJS1126)资助 （90820002,60903100,61005008）
