MOTIVATION Microarray technology emerges as a powerful tool in life science. One major application of microarray technology is to identify differentially expressed genes under various conditions. Currently, the statistical methods to analyze microarray data are generally unsatisfactory, mainly due to the lack of understanding of the distribution and error structure of microarray data. RESULTS...

This paper presents an adaptive Generalized Likelihood Ratio (GLR) test for multiple Faults Detection and Isolation (FDI) in stochastic linear dynamic systems. Based on the work of Willsky and Jones (1976), we propose a modified generalized likelihood ratio test, allowing detection, isolation and estimation of multiple sequential faults. Our contribution aims to maximise the good decision rate ...

Since the first approach [Wright and Wrigley 1991]of combining a probabilistic method into the GLR technique was published, Some probabilistic GLR parsers also have been implemented in which probabilities are assigned to actions of LR parsing tables by using lookaheads or LR states as simple context information of [Briscoe and Carroll 1993], [Kentaro et al. 1998], and [Ruland, 2000] which does ...

In this paper we formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise. We derive the generalized likelihood ratio (GLR) for each problem in the class. We then establish the invariances for the GLR and argue that these are the natural invariances for the problem. In each case, the GLR is a maximal invariant statistic, and the distribut...

To deal with the problem of sensitivity to noise in semi-supervised learning for biometrics, this paper proposes a robust Gaussian-Laplacian Regularized (GLR) framework based on maximum correntropy criterion (MCC), called GLR-MCC, along with its convergence analysis. The half quadratic (HQ) optimization technique is used to simplify the correntropy optimization problem to a standard semi-superv...

in this paper a new algorithm is presented for coherent radar detection of targets, when distribution of the interference (clutter and noise) is non-gaussian. the neyman-pearson criterion is used for optimal detection and several successive received samples from a radar range cell are used for detection in the same cell. on the basis of recent empirical evidences, the interference distribution ...

An extension of the well-known probabilistic neural network (PNN) to generalized locally recurrent PNN (GLR PNN) is introduced. The GLR PNN is derived from the original PNN by incorporating a fully connected recurrent layer between the pattern and output layers. This extension renders GLR PNN sensitive to the context in which events occur, and therefore, capable of identifying temporal and spat...

This paper addresses a target detection problem in radar imaging for which the covariance matrix of unknown Gaussian clutter has block diagonal structure. This block diagonal structure is the consequence of a target lying along a boundary between two statistically independent clutter regions. Here, we design adaptive detection algorithms using both the generalized likelihood ratio (GLR) and the...