We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cyclic parameters of the optimization method to get a balance between local and global...

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between sp...

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has a poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. In this work, we p...

This paper deals with the unsupervised classification of univariate observations. Given a set of observations originating from a K -component mixture, we focus on the estimation of the component expectations. We propose an algorithm based on the minimization of the “K -product” (KP) criterion we introduced in a previous work. We show that the global minimum of this criterion can be reached by f...

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthet...

Data-based control design methods most often consist of iterative adjustment of the controller’s parameters towards the parameter values which minimize an H2 performance criterion. Typically, batches of input–output data collected from the system are used to feed directly a gradient descent optimization — no process model is used. A limiting factor in the application of these methods is the lac...

This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. T...

In this paper, we propose to study the problem of noisy source coding/denoising. The challenge of this problem is that a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. Most of the bibliography in this domain is based on the fact that, for a specific criterion, the ...

This paper deals with the clustering of univariate observations: given a set of observations coming from K possible clusters, one has to estimate the cluster means. We propose an algorithm based on the minimization of the ”KP” criterion we introduced in a previous work. In this paper, we show that the global minimum of this criterion can be reached by first solving a linear system then calculat...