کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4947466 1439578 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Integrating feature and graph learning with low-rank representation
ترجمه فارسی عنوان
ادغام ویژگی و گراف یادگیری با نمایندگی رتبه پایین
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
We propose a new subspace clustering method that integrates feature and manifold learning while learning a low-rank representation of the data in a single model. This new model seeks a low-rank representation of the data using only the most relevant features in both linear and nonlinear spaces, which helps reveal more accurate data relationships in both linear and nonlinear spaces, because data relationships can be less afflicted by irrelevant features. Moreover, the graph Laplacian is updated according to the learning process, which essentially differs from existing nonlinear subspace clustering methods that require constructing a graph Laplacian as an independent preprocessing step. Thus the learning processes of features and manifold mutually enhance each other and lead to powerful data representations. Extensive experimental results confirm the effectiveness of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 249, 2 August 2017, Pages 106-116
نویسندگان
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