Model-based SIR for dimension reduction

Model-based SIR for dimension reduction

This document can be viewed with any pdf reader. However, the animation in Figures 6 and 7 can only be viewed with a recent version of Adobe Reader.

Model Based Approaches for Simultaneous Dimension Reduction and Clustering

High dimensional data are regularly generated from various sources. Thus subsets of data are usually concentrated in different subspaces. It is of interest to develop methods that achieve simultaneous dimension reduction and clustering. In this talk we will present a new solution based on a constrained version of mixture of factor analyzers. Our proposed technique imposes constraints on the und...

Model selection for partial least squares based dimension reduction

0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.11.009 ⇑ Corresponding author. Address: Department of C Tongji University, Cao’an Road 4800, Shanghai 2018 3706; fax: +86 21 6958 9241. E-mail address: [email protected] (M. You). Partial least squares (PLS) has been widely applied to process scientific data sets as an effective dimension reduction technique. The main...

A mixture model for dimension reduction

Model Approximation via Dimension Reduction

In the initial stages of refining a mathematical model of a real-world dynamical system, one is often confronted with many more variables and coupled differential equations than one intuitively feels should be sufficient to describe the system. Yet none of the variables may seem so irrelevant as to be excludable nor so dominant as to explain the overall dynamics. Part of the problem might even ...