This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 189
Type: Invited
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #307385
Title: Energy Functions for Dimension Reduction and Graph Visualization
Author(s): Lisha Chen*+
Companies: Yale University
Address: 24 Hillhouse Avenue, New Haven , CT, 06520-8290,
Keywords: MDS ; nonlinear dimension reduction ; clustering

We introduce a parametrized family of energy functions useful for proximity analysis, nonlinear dimension reduction, and graph drawing. The functions are inspired by the physics intuitions of attractive and repulsive forces common in graph drawing. Their minimization generates low-dimensional configurations (embeddings, graph drawings) whose interpoint distances match input distances as best as possible. The problem of selecting an energy/stress function is translated to a parameter selection problem which can be approached with a meta-criterion. Of particular interest is the tuning of a parameter associated with the notion of "clustering strength". Such tuning greatly helps identifying clusters.

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