کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
402301 676897 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prior class dissimilarity based linear neighborhood propagation
ترجمه فارسی عنوان
انتشار محله خطی متشکل از طبقه متضاد پیشین
کلمات کلیدی
یادگیری نیمه نظارتی، طبقه بندی، روش مبتنی بر گراف، تبادل خطی محله، اطلاعات پیشین، ناهماهنگی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

The insufficiency of labeled training data for representing the distribution of entire dataset is a major obstacle in various practical data mining applications. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, provide possibilities to solve this problem. Graph-based semi-supervised learning has recently become one of the most active research areas. In this paper, a novel graph-based semi-supervised learning approach entitled Class Dissimilarity based Linear Neighborhood Propagation (CD-LNP) is proposed, which assumes that each data point can be linearly reconstructed from its neighborhood. The neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. Our algorithm can propagate the labels from the labeled points to entire data set using these linear neighborhoods with sufficient smoothness. Experiment results demonstrate that our approach outperforms other popular graph-based semi-supervised learning methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 83, July 2015, Pages 58–65
نویسندگان
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