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...
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
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...
: In the development of software systems, quality properties should be considered along with the development process so that the qualities of software systems can be inferred and p...
In this paper, we introduce a component-based design methodology and present a practical analysis approach that makes use of the modular nature of component-based designs to allev...