DEMScale: Large Scale MDS Accounting for a Ridge Operator and Demographic Variables

Authors:
Stephen L. France;J. Douglas Carroll
Affiliations:
Lubar School of Business, UW --- Milwaukee, Milwaukee 53201-0742;Graduate School of Management, Rutgers University, Newark, New Jersey 07102-3027
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

Citing 15
Cited 0

Spoken letter recognition

NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A linear iteration time layout algorithm for visualising high-dimensional data

Proceedings of the 7th conference on Visualization '96
Ridge regression: biased estimation for nonorthogonal problems

Technometrics
A Hybrid Layout Algorithm for Sub-Quadratic Multidimensional Scaling

INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
GGobi: evolving from XGobi into an extensible framework for interactive data visualization

Computational Statistics & Data Analysis - Data visualization
Fast multidimensional scaling through sampling, springs and interpolation

Information Visualization
Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces

The Journal of Machine Learning Research
Multiscale hybrid MDS

IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Steerable, Progressive Multidimensional Scaling

INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark

Pattern Recognition Letters
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
Eigensolver methods for progressive multidimensional scaling of large data

GD'06 Proceedings of the 14th international conference on Graph drawing
An accurate MDS-Based algorithm for the visualization of large multidimensional datasets

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
MetricMap: an embedding technique for processing distance-based queries in metric spaces

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a method called DEMScale is introduced for large scale MDS. DEMScale can be used to reduce MDS problems into manageable sub-problems, which are then scaled separately. The MDS items can be split into sub-problems using demographic variables in order to choose the sections of the data with optimal and sub-optimal mappings. The lower dimensional solutions from the scaled sub-problems are recombined by taking sample points from each sub-problem, scaling the sample points, and using an affine mapping with a ridge operator to map the non-sample points. DEMScale builds upon the methods of distributional scaling and FastMDS, which are used to split and recombine MDS mappings. The use of a ridge regression parameter enables DEMScale to achieve stronger solution stability than the basic distributional scaling and FastMDS techniques. The DEMScale method is general, and is independent of the MDS technique and optimization method used.