کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406861 678114 2014 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Graphical lasso quadratic discriminant function and its application to character recognition
ترجمه فارسی عنوان
تابع عددی متغیر گرافیکی لسو و کاربرد آن در شناخت شخصیت
کلمات کلیدی
گرافیک لازو، عملکرد تشخیصی چهارگانه، شخصیت شناسی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Multivariate Gaussian distribution is a popular assumption in many pattern recognition tasks. The quadratic discriminant function (QDF) is an effective classification approach based on this assumption. An improved algorithm, called modified QDF (or MQDF in short) has achieved great success and is widely recognized as the state-of-the-art method in character recognition. However, because both of the two approaches estimate the mean and covariance by the maximum-likelihood estimation (MLE), they often lead to the loss of the classification accuracy when the number of the training samples is small. To attack this problem, in this paper, we engage the graphical lasso method to estimate the covariance and propose a new classification method called the graphical lasso quadratic discriminant function (GLQDF). By exploiting a coordinate descent procedure for the lasso, GLQDF can estimate the covariance matrix (and its inverse) more precisely. Experimental results demonstrate that the proposed method can perform better than the competitive methods on two artificial and nine real datasets (including both benchmark digit and Chinese character data).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 129, 10 April 2014, Pages 33–40
نویسندگان
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