Matthew J. Hirn

Learn More
In this short letter we present the construction of a bi-stochastic kernel p for an arbitrary data set X that is derived from an asymmetric affinity function α. The affinity function α measures the similarity between points in X and some reference set Y. Unlike other methods that construct bi-stochastic kernels via some convergent iteration process or(More)
  • J J Benedetto, W Czaja, M Ehler, C Flake, M Hirn
  • 2010
State of the art dimension reduction and classification schemes in multi-and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each(More)
We consider the following interpolation problem. Suppose one is given a finite set E ⊂ R d , a function f : E → R, and possibly the gradients of f at the points of E. We want to interpolate the given information with a function F ∈ C 1,1 (R d) with the minimum possible value of Lip(∇F). We present practical, efficient algorithms for constructing an F such(More)
  • 1