kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
Analyzing the effect of concentrated noise on a typical decision-making process of a simplified two-candidate voting model, we have demonstrated that a local approach using a regi...
Both parametric design tasks and analysis tasks of technical systems have a similar problem setting: The structure of the system to be configured or analyzed is defined already. W...
In this paper we address the problem of how to learn a structural prototype that can be used to represent the variations present in a set of trees. The prototype serves as a patte...
This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-...