High breakdown mixture discriminant analysis
نویسندگان
چکیده
منابع مشابه
High Breakdown Linear Discriminant Analysis
The classiication rules of linear discriminant analysis are deened by the true mean vectors and the common covariance matrix of the populations from which the data come. As these true parameters are in general unknown, they are commonly estimated by the sample mean vector and covariance matrix of the data in a training sample randomly drawn from each population. These sample statistics are howe...
متن کاملRegularized mixture discriminant analysis
In this paper we seek a Gaussian mixture model (GMM) of the classconditional densities for plug-in Bayes classification. We propose a method for setting the number of the components and the covariance matrices of the class-conditional GMMs. It compromises between simplicity of the model selection based on the Bayesian information criterion (BIC) and the high accuracy of the model selection base...
متن کاملHigh Dimensional Discriminant Analysis
We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt the Gaussian framework to high dimensio...
متن کاملAdaptive Mixture Discriminant Analysis for Supervised Learning with Unobserved Classes
In supervised learning, an important issue usually not taken into account by classical methods is the possibility of having in the test set individuals belonging to a class which has not been observed during the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one of the known classes in the training set and will not be able to detect ne...
متن کاملMicrosoft Word - Model based Mixture Discriminant Analysis-An Exprimental –
The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2005
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2003.12.003