کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4630898 1340611 2011 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Large correlation analysis
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
Large correlation analysis
چکیده انگلیسی

In this paper, a novel supervised dimensionality reduction method is developed based on both the correlation analysis and the idea of large margin learning. The method aims to maximize the minimal correlation between each dimensionality-reduced instance and its class label, thus named as large correlation analysis (LCA). Unlike most existing correlation analysis methods such as CCA, CCAs and CDA, which all maximize the total or ensemble correlation over all training instances, LCA devotes to maximizing the individual correlations between given instances and its associated labels and is established by solving a relaxed quadratic programming with box-constraints. Experimental results on real-world datasets from both UCI and USPS show its effectiveness compared to the existing canonical correlation analysis methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 217, Issue 22, 15 July 2011, Pages 9041–9052
نویسندگان
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