Data dimensionality estimation methods: a survey

Data dimensionality estimation methods: a survey

In this paper, data dimensionality estimation methods are reviewed. The estimation of the dimensionality of a data set is a classical problem of pattern recognition. There are some good reviews [1] in literature but they do not include more recent developments based on fractal techniques and neural autoassociators. The aim of this paper is to provide an up-to-date survey of the dimensionality e...

Resampling Methods in Complex Survey Data

Intrinsic Dimensionality Estimation in Visualizing Toxicity Data

Over the years, a number of dimensionality reduction techniques have been proposed and used in chemo informatics to perform nonlinear mappings. Nevertheless, data visualization techniques can be efficiently applied for dimensionality reduction mainly in a case if the data are not really high-dimensional and can be represented as a nonlinear low-dimensional manifold when it is possible to reduce...

Comparison of Estimation Methods for Complex Survey Data Analysis

Recently structural equation modeling software packages have implemented more accurate statistical methodology for analyzing complex survey data. The computational algorithms however vary across the packages and produce different results even for simple models. In this note we conduct simulation studies to compare the performance of the methods implemented in Mplus and LISREL. The Mplus algorit...

A Survey of Nonlinear Attitude Estimation Methods

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filte...