Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computati...
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas...
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
This paper analyzes the Fourier model reduction (FMR) method from a rational Krylov projection framework and shows how the FMR reduced model, which has guaranteed stability and a ...
We illustrate recent progress in developing algorithms for solving the Kohn-Sham problem. Key ingredients of our algorithm include pseudopotentials implemented on a real space gri...
James R. Chelikowsky, Murilo L. Tiago, Yousef Saad...
In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks t...