کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
413082 679723 2006 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Local regularization assisted orthogonal least squares regression
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Local regularization assisted orthogonal least squares regression
چکیده انگلیسی

A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in the regression model with an individual regularization parameter, the ability for the orthogonal least squares model selection to produce a very sparse model with good generalization performance is greatly enhanced. Furthermore, with the assistance of local regularization, when to terminate the model selection procedure becomes much clearer. A comparison with a state-of-the-art method for constructing sparse regression models, known as the relevance vector machine, is given. The proposed LROLS algorithm is shown to possess considerable computational advantages, including well conditioned solution and faster convergence speed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 69, Issues 4–6, January 2006, Pages 559–585
نویسندگان
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