کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8965193 1646702 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Symmetric low-rank representation with adaptive distance penalty for semi-supervised learning
ترجمه فارسی عنوان
نمایش ضعیفانه متقارن با مجازات فاصله سازگار برای یادگیری نیمه نظارت
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Graph-based approaches have been successfully used in semi-supervised learning (SSL) by weighting the affinity between the corresponding pairs of samples. Low-rank representation (LRR) is one of the state-of-the-art methods, has been extensively employed in the existing graph-based learning models. In graph-based SSL, a weighted affinity graph induced by LRR coefficients is used to reflect the relationships among data samples. Constructing a robust and discriminative graph to discover the intrinsic structures of the data is critical for SSL, because the central idea behind the graph-based SSL approaches is to explore the pairwise affinity between data samples to infer those unknown labels. However, most of existing LRR-based approaches fail to guarantee weight consistency for each pair of data samples, which is beneficial to preserve the subspace structures of high dimensional data. In this paper, we propose a symmetric LRR with adaptive distance penalty (SLRRADP) method for the small sample size (SSS) recognition problem. The graph identified by SLRRADP can not only preserve global and local structures of data, but also exploit intrinsic correlation information of data samples. Extensive experimental results on semi-supervised classification tasks demonstrate the effectiveness of the proposed method and its superiority in performance over the state-of-the-art graph construction approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 316, 17 November 2018, Pages 376-385
نویسندگان
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