Localized inverse factorization

Localized Sliced Inverse Regression

We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classification problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.

Recursive least squares with stabilized inverse factorization

Recently developed recursive least squares schemes, where the square root of both the covariance and the information matrix are stored and updated, are known to be particularly suited for parallel implementation. However, when finite precision arithmetic is used, round-off errors apparently accumulate unboundedly, so that after a number of updates the computed least squares solutions turn out t...

Occluded Image Object Recognition using Localized Nonnegative Matrix Factorization Methods

The factorization method for a class of inverse elliptic problems

In this paper the factorization method from inverse scattering theory and impedance tomography is extended to a class of general elliptic differential equations in divergence form. The inverse problem is to determine the interface ∂Ω of an interior change of the material parameters from the Neumann-Dirichlet map. Since absorption is allowed a suitable combination of the real and imaginary part ...

A factorization of the inverse of the shifted companion matrix