Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length/data likelihood). For problems such as change detection, unsupervised segmentation or data clustering it is common p...

logistics outsourcing has been at the top of the management agenda during recent decades. the selection of the proper service supplier is the key to success in logistic outsourcing firms could select the right supplier by applying appropriate methods and selection criteria. in this paper a new framework is proposed on the basis of weighted additive fuzzy programming approach and linear programm...

Factor analysis is a statistical procedure to describe observed data in terms of unobserved variables called factors. Naturally, it is necessary to determine the number of factors to represent the system. There are several existent criteria to deal with the tradeoff between reduction of approximation error and avoidance of overparameterization. However, given the factors there is a lack of an a...

The problem of model selection is inevitable in an increasingly large number of applications involving partial theoretical knowledge and vast amounts of information, like in medicine, biology or economics. The associated techniques are intended to determine which variables are “important” to “explain” a phenomenon under investigation. The terms “important” and “explain” can have very different ...

Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level score), which is calculated in a trace ratio form. Since the number of all possible feature subsets is very huge, it is often prohibitively expens...

For a given source distribution, we establish properties of the conditional density achieving the rate distortion function lower bound as the distortion parameter varies. In the limit as the distortion tolerated goes to zero, the conditional density achieving the rate distortion function lower bound becomes degenerate in the sense that the channel it defines becomes error-free. As the permitted...