کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11031550 | 1645970 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Unsupervised multi-view feature extraction with dynamic graph learning
ترجمه فارسی عنوان
استخراج ویژگی های چند جانبه بدون نظارت با یادگیری گراف پویا
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کلمات کلیدی
استخراج ویژگی های چند نمایشه، روابط نمونه درونی، یادگیری گراف پویا،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Graph-based multi-view feature extraction has attracted much attention in literature. However, conventional solutions generally rely on a manually defined affinity graph matrix, which is hard to capture the intrinsic sample relations in multiple views. In addition, the graph construction and feature extraction are separated into two independent processes which may result in sub-optimal results. Furthermore, the raw data may contain adverse noises that reduces the reliability of the affinity matrix. In this paper, we propose a novel Unsupervised Multi-view Feature Extraction with Dynamic Graph Learning (UMFE-DGL) to solve these limitations. We devise a unified learning framework which simultaneously performs dynamic graph learning and the feature extraction. Dynamic graph learning adaptively captures the intrinsic multiple view-specific relations of samples. Feature extraction learns the projection matrix that could accordingly preserve the dynamically adjusted sample relations modelled by graph into the low-dimensional features. Experimental results on several public datasets demonstrate the superior performance of the proposed approach, compared with state-of-the-art techniques.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 256-264
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 256-264
نویسندگان
Dan Shi, Lei Zhu, Zhiyong Cheng, Zhihui Li, Huaxiang Zhang,