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 ]

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Description:

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 mloss.org.

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

Other available revisons

Version Changelog Date
1.2
  • Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

  • Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

  • Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013

July 27, 2013, 14:35:36
1.1
  • Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

  • Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

March 11, 2013, 11:47:39
1.0

Initial Announcement on mloss.org.

December 4, 2012, 16:49:24

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