کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6938779 1449965 2018 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data-independent Random Projections from the feature-space of the homogeneous polynomial kernel
ترجمه فارسی عنوان
پیش بینی های تصادفی داده مستقل از فضای ویژگی یک هسته چندجملهای همگن
کلمات کلیدی
پیش بینی تصادفی، هسته چند جملهای همگن، کاهش ابعاد غیر خطی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Performing a Random Projection from the feature space associated to a kernel function may be important for two main reasons. (1) As a consequence of the Johnson-Lindestrauss lemma, the resulting low-dimensional representation will preserve most of the structure of data in the kernel feature space and (2) an efficient linear classifier trained on transformed data might approximate the accuracy of its nonlinear counterparts. In this paper, we present a novel method to perform Random Projections from the feature space of homogeneous polynomial kernels. As opposed to other kernelized Random Projection proposals, our method focuses on a specific kernel family to preserve some of the beneficial properties of the original Random Projection algorithm (e.g. data independence and efficiency). Our extensive experimental results evidence that the proposed method efficiently approximates a Random Projection from the kernel feature space, preserving pairwise distances and enabling a boost on linear classification accuracies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 82, October 2018, Pages 130-146
نویسندگان
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