کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6938774 | 1449965 | 2018 | 38 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering
ترجمه فارسی عنوان
یک روش تقسیم بندی ماتریس متعامد غیرخطی متعامد به خوشه بندی زیر فضایی
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کلمات کلیدی
خوشه بندی فضای مجاز، تقسیم ماتریس غیر منفی، ارگانوگرافی، هسته، تنظیم مقادیر گراف،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernel-based orthogonal multiplicative update rules to solve the subspace clustering problem. In nonlinear orthogonal NMF framework, we propose two subspace clustering algorithms, named kernel-based non-negative subspace clustering KNSC-Ncut and KNSC-Rcut and establish their connection with spectral normalized cut and ratio cut clustering. We further extend the nonlinear orthogonal NMF framework and introduce a graph regularization to obtain a factorization that respects a local geometric structure of the data after the nonlinear mapping. The proposed NMF-based approach to subspace clustering takes into account the nonlinear nature of the manifold, as well as its intrinsic local geometry, which considerably improves the clustering performance when compared to the several recently proposed state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 82, October 2018, Pages 40-55
Journal: Pattern Recognition - Volume 82, October 2018, Pages 40-55
نویسندگان
Dijana ToliÄ, Nino Antulov-Fantulin, Ivica Kopriva,