کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
531436 869843 2008 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A study of regularized Gaussian classifier in high-dimension small sample set case based on MDL principle with application to spectrum recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A study of regularized Gaussian classifier in high-dimension small sample set case based on MDL principle with application to spectrum recognition
چکیده انگلیسی

In classifying high-dimensional patterns such as stellar spectra by a Gaussian classifier, the covariance matrix estimated with a small-number sample set becomes unstable, leading to degraded classification accuracy. In this paper, we investigate the covariance matrix estimation problem for small-number samples with high dimension setting based on minimum description length (MDL) principle. A new covariance matrix estimator is developed, and a formula for fast estimation of regularization parameters is derived. Experiments on spectrum pattern recognition are conducted to investigate the classification accuracy with the developed covariance matrix estimator. Higher classification accuracy results are obtained and demonstrated in our new approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 41, Issue 9, September 2008, Pages 2842–2854
نویسندگان
, , ,