Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
This paper describes an extension to the componentbased programming model to support real-time dynamic guarantee for distributed applications. The extended model aims to include a...
Abstract. This article presents a unified theory for analysis of components in discrete data, and compares the methods with techniques such as independent component analysis, non-...
This paper discusses concept lattices and some of their applications in component library development and compiler optimizations. Ongoing work on concept-based userextensible simp...
Sibylle Schupp, D. P. Gregor, B. Osman, David R. M...
In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combi...