کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495242 862821 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Extended semi-supervised fuzzy learning method for nonlinear outliers via pattern discovery
ترجمه فارسی عنوان
روش فراشناختی نیمه نظارتی فازی برای تخمین های غیر خطی از طریق کشف الگو
کلمات کلیدی
تجزیه و تحلیل فیزیکی، برآورد پارامتر، موارد خارج از منزل، خوشه بندی نیمه نظارت، به رسمیت شناختن تصویر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• A parameterized fuzzy LDA is proposed as semi-supervised learning precursor.
• Apply Hopfield Neural Network to dynamic parameter estimation.
• Separate outlier instances from the whole feature space.
• Obtain the initial fuzzy classification of regular feature space.
• Semi-supervised fuzzy clustering algorithm is presented on the basis of class discriminatory measure for the nonlinear outliers.

This article presents an extended Parameterized Fuzzy Semi-supervised learning (PFSL) method, in which the key innovation is the capability of separating a sample set into two independent subsets: outlier sample subset and regular sample subset. In our proposed PFSL, we first develop an improved parameterized Fuzzy Linear Discriminant Analysis (F-LDA) algorithm to classify regular samples, in which the distribution information of each sample in terms of fuzzy membership degree is incorporated with the redefined within-class and between-class scatter matrices. To achieve good parameter estimation for this improved F-LDA, we advocate the use of Hopfield Neural Networks (HNN) due to its efficiency. Second, a new semi-supervised Fuzzy C-Means (S-FCM) algorithm is designed using pre-computed cluster number and cluster centers in the supervised pattern discovery stage. It is applied to classify the remaining outlier samples and generate the final classification result. Third, since Kernel Fisher Discriminant (KFD) is an efficient way to extract nonlinear discriminant features, a kernel version of the proposed PFSL (K-PFSL) is discussed. Extensive experiments on the ORL, NUST603, FERET and Yale face datasets show the effectiveness and the superiority of the proposed algorithm.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 29, April 2015, Pages 245–255
نویسندگان
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