کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406535 678092 2014 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A subset method for improving Linear Discriminant Analysis
ترجمه فارسی عنوان
یک روش زیرمجموعه برای بهبود تجزیه و تحلیل خطی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• We use the subset method to greatly improve the performance of Linear Discriminant Analysis in low-dimensional representations.
• We propose a new classifier-specific based partition method.
• Graph cut is adopted to solve the partition method.
• The method could also be used to improve other linear discriminant algorithms.

Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it suffers from class separation problem for C  -class when the reduced dimensionality is less than C−1C−1. To cope with this problem, we propose a subset improving method in this paper. In the method, the subspaces are found for each subset rather than that for the entire data set. To partition the entire data set into subsets, a cost matrix is first estimated from the training set with the pre-learned classifier, then the graph cut method is adopted to minimize the cost between each subset. We use LDA to find subspaces for each subset. Experimental results based on different applications demonstrate both the generality and effectiveness of the proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 138, 22 August 2014, Pages 310–315
نویسندگان
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