Integrative linear discriminant analysis with guaranteed error rate improvement

Multiple Group Linear Discriminant Analysis: Robustness and Error Rate

Abstract: Discriminant analysis for multiple groups is often done using Fisher’s rule, and can be used to classify observations into different populations. In this paper, we measure the performance of classical and robust Fisher discriminant analysis using the Error Rate as a performance criterion. We were able to derive an expression for the optimal error rate in the situation of three groups....

Multiple group discriminant analysis: Robustness and error rate

Unbiased bootstrap error estimation for linear discriminant analysis

Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic argume...

Improvement of Linear Discriminant Analysis by Applying The Ensemble Method

Linear Discriminant Analysis (LDA) is one of the learning algorithms for the binary problems. One of the drawbacks of LDA is the degradation of its performance when it is difficult to approximate the boundary between two classes with a linear function, i.e. the boundary is non-linear. In this research, we apply Error-Correcting Output Codes (ECOC) to LDA. ECOC is well-known as one of the ensemb...

Balanced Truncation for Stochastic Linear Systems with Guaranteed Error Bound

We consider model order reduction of stochastic linear systems by balanced truncation. Two types of Gramians are suggested, both satisfying generalized Lyapunov equations. The first is motivated by energy functionals, the second is taylored to yield an error bound for the truncated system.