Title of article :
Heteroscedastic linear feature extraction based on sufficiency conditions
Author/Authors :
Mahanta، نويسنده , , Mohammad Shahin and S. Aghaei، نويسنده , , Amirhossein and Plataniotis، نويسنده , , Konstantinos N. and Pasupathy، نويسنده , , Subbarayan، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
10
From page :
821
To page :
830
Abstract :
Classification of high-dimensional data typically requires extraction of discriminant features. This paper proposes a linear feature extractor, called whitened linear sufficient statistic (WLSS), which is based on the sufficiency conditions for heteroscedastic Gaussian distributions. WLSS approximates, in the least squares sense, an operator providing a sufficient statistic. The proposed method retains covariance discriminance in heteroscedastic data, while it reduces to the commonly used linear discriminant analysis (LDA) in the homoscedastic case. Compared to similar heteroscedastic methods, WLSS imposes a low computational complexity, and is highly generalizable as confirmed by its consistent competence over various data sets.
Keywords :
Gaussianity , Quadratic classifier , feature extraction , sufficient statistic , dimension reduction , Heteroscedastic data , Discriminant analysis
Journal title :
PATTERN RECOGNITION
Serial Year :
2012
Journal title :
PATTERN RECOGNITION
Record number :
1734337
Link To Document :
بازگشت