کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947631 | 1439589 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Ellipsoidal data description
ترجمه فارسی عنوان
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Support vector data description (SVDD) is a leading classification method for novelty detection, which minimizes the volume of a spherically shaped decision boundary around the normal class. While SVDD has achieved promising performance, it will lead to a loose boundary for multivariate datasets of which the input dimensions are usually correlated. Inspired by the relationship between kernel principal component analysis (kernel PCA) and the best-fit ellipsoid for a dataset, this study proposes the ellipsoidal data description (ELPDD) which considers feature variance of each dimension adaptively. A minimum volume enclosing ellipsoid (MVEE) is constructed around the target data in the kernel PCA subspace which can be learned via a SVM-like objective function with log-determinant penalty. We also provide the Rademacher complexity bound for our model. Some relating problems are investigated in detail. Experiments on artificial and real-world datasets validate the effectiveness of our method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 328-339
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 328-339
نویسندگان
Kunzhe Wang, Huaitie Xiao,