Classification with high dimensional features
نویسندگان
چکیده
منابع مشابه
Bayesian Classification and Regression with High Dimensional Features
This thesis responds to the challenges of using a large number, such as thousands, of features in regression and classification problems. There are two situations where such high dimensional features arise. One is when high dimensional measurements are available, for example, gene expression data produced by microarray techniques. For computational or other reasons, people may select only a sma...
متن کاملBayesian Classification and Regresssion with High Dimensional Features
This thesis responds to the challenges of using a large number, such as thousands, of features in regression and classification problems. There are two situations where such high dimensional features arise. One is when high dimensional measurements are available, for example, gene expression data produced by microarray techniques. For computational or other reasons, people may select only a sma...
متن کاملClassification with Ultrahigh-Dimensional Features
Although much progress has been made in classification with high-dimensional features [10, 16, 6, 47], classification with ultrahighdimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-featur...
متن کاملHigh-dimensional Classification Using Features Annealed Independence Rules 1
Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is poorly understood. In a seminal paper, Bickel and Levina [Bernoulli 10 (2004) 989–1010] show that the Fisher discriminant performs poorly due to diverging spectra ...
متن کاملHigh Dimensional Classification Using Features Annealed Independence Rules.
Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose...
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ژورنال
عنوان ژورنال: WIREs Computational Statistics
سال: 2018
ISSN: 1939-5108,1939-0068
DOI: 10.1002/wics.1453