Project details for cbMDS Correlation Based Multi Dimensional Scaling

Screenshot cbMDS Correlation Based Multi Dimensional Scaling 1.0

by emstrick - December 4, 2012, 16:49:24 CET [ BibTeX BibTeX for corresponding Paper Download ]

view ( today), download ( today ), 0 subscriptions


Correlation-based multidimensional scaling is implemented for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space are directly maximized by gradient methods. Alternatively, optimization of Spearman rank correlation is achieved by a numerically soft formulation. Scale and shift invariance properties of correlation help circumventing typical distance crowding problems.

Contrary to nonmetric MDS based on isotonic regression and high-throughput MDS (HiT-MDS) maximizing Pearson correlation, the implementation is not matrix-conditioned (global) but row-conditioned (local) which allows for embedding asymmetric of relational score matrices. That is, instance-specific similarity profiles are reconstructed rather than global rank or distance relationships.

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Platform Independent
Data Formats: Matlab
Tags: Data Visualization, Mds, Neighbor Embedding
Archive: download here


No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.