L 2 Model Reduction and Variance Reduction

L2 Model reduction and variance reduction

In this contribution we examine certain variance properties of model reduction. The focus is on L2 model reduction, but some general results are also presented. These general results can be used to analyze various other model reduction schemes. The models we study are nite impulse respons (FIR) and output error (OE) models. We compare the variance of two estimated models. The rst one is estimat...

Variance Reduction via Lattice

This is a review article on lattice methods for multiple integration over the unit hypercube, with a variance-reduction viewpoint. It also contains some new results and ideas. The aim is to examine the basic principles supporting these methods and how they can be used eeectively for the simulation models that are typically encountered in the area of Management Science. These models can usually ...

Variance Reduction Techniques in . . .

Iterative - interpolation algorithms for L 2 model reduction ∗ by

This paper is concerned with the construction of reduced–order models for high–order linear systems in such a way that the L2 norm of the impulse–response error is minimized. Two convergent algorithms that draw on previous procedures presented by the same authors, are suggested: one refers to s–domain representations, the other to time–domain state–space representations. The algorithms are base...

Weighted Maximum Variance Dimensionality Reduction

Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction met...