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中文摘要：
建立一种离群样本划分的半监督模糊学习算法模型。首先，提出一种基于Hopfield参数估计的松弛条件模糊鉴别分析算法，重新定义每一个样本的隶属度，并在特征抽取的过程中，根据隶属度对散布矩阵的定义所做的贡献获得每个样本相应的类别信息，由此获得普通样本分类信息。其次，根据样本隶属度的分布信息划分出离群样本空间，将普通样本分类结果作为离群样本聚类的先验类属信息，并对该空间样本提出一种新的半监督模糊学习策略进行动态聚类。该算法同时具备了监督学习和无监督学习方法的优势，克服了传统聚类缺乏类过程知识的缺点，可以有效地解决特征空间中特殊样本的分类问题。性能分析表明，该方法优于单一的特征抽取方法，在NUST603、ORL、XM2VTS和FERET人脸数据库上的识别性能均得到有效提高。
英文摘要：
In this paper, a semi-supervised fuzzy learning algorithm based on the partitioning of the outlier feature space is presented. First, a reformative fuzzy LDA algorithm using a relaxed normalized condition is proposed to achieve the distri-bution information of each sample represented by a fuzzy membership degree, which is incorporated into the redefinition of the scatter matrices. Moreover, we approach the problem of parameter estimation by considering the formulation of the Hopfield neural network. Using this method, the first key step of the fuzzy classification is addressed. Second, considering the negative influences from the outlier instances, we separate the outliers from the whole feature space by means of the dis- tribution information of each sample. The strength of the technique is that it successfully uses the improved fuzzy supervised algorithm as a feature extraction tool, while quantifying those factors that exert influence ons the outlier class assignment, by means of the fuzzy semi-supervised method. Extensive experimental studies conducted on the NUST603, ORL, XM2VTS and FERET face image databases show that the effectiveness of the proposed fuzzy integrated algorithm.
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