کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946794 1439418 2017 50 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An online incremental orthogonal component analysis method for dimensionality reduction
ترجمه فارسی عنوان
یک روش تجزیه و تحلیل مولکولی مجتمع آنلاین برای کاهش ابعاد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In this paper, we introduce a fast linear dimensionality reduction method named incremental orthogonal component analysis (IOCA). IOCA is designed to automatically extract desired orthogonal components (OCs) in an online environment. The OCs and the low-dimensional representations of original data are obtained with only one pass through the entire dataset. Without solving matrix eigenproblem or matrix inversion problem, IOCA learns incrementally from continuous data stream with low computational cost. By proposing an adaptive threshold policy, IOCA is able to automatically determine the dimension of feature subspace. Meanwhile, the quality of the learned OCs is guaranteed. The analysis and experiments demonstrate that IOCA is simple, but efficient and effective.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 85, January 2017, Pages 33-50
نویسندگان
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