کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653610 | 679206 | 2005 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Subset based least squares subspace regression in RKHS
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کلمات کلیدی
Nyström approximationReproducing kernel Hilbert space - بازسازی هسته فضای هیلبرتEigenspaces - به ویژه اقدام نشودKernel principal component analysis - تجزیه و تحلیل مولفه اصلی هستهKernel canonical correlation analysis - تجزیه و تحلیل همبستگی کانونی هستهKernel methods - روش های هسته ایRegression - رگرسیونSubspaces - زیرمجموعه هاKernel partial least squares - هسته حداقل مربعات جزئی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Kernel based methods suffer from exceeding time and memory requirements when applied on large datasets since the involved optimization problems typically scale polynomially in the number of data samples. As a remedy, some least squares methods on one hand only reduce the number of parameters (for fast training), on the other hand only work on a reduced set (for fast evaluation). Departing from the Nyström based feature approximation, via the fixed-size LS-SVM model, we propose a general regression framework, based on restriction of the search space to a subspace and a particular choice of basis vectors in feature space. In the general model both reduction aspects are unified and become explicit model choices. This allows to accommodate kernel Partial Least Squares and kernel Canonical Correlation analysis for regression with a sparse representation, which makes them applicable to large data sets, with little loss in accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 63, January 2005, Pages 293-323
Journal: Neurocomputing - Volume 63, January 2005, Pages 293-323
نویسندگان
L. Hoegaerts, J.A.K. Suykens, J. Vandewalle, B. De Moor,