Project details for cbMDS Correlation Based Multi Dimensional Scaling

Screenshot cbMDS Correlation Based Multi Dimensional Scaling 1.1

by emstrick - March 11, 2013, 11:47:39 CET [ BibTeX BibTeX for corresponding Paper Download ]

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The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (asymmetric) distances, dissimilarities, or (negative) scores. Input-output relations are modelled as row-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

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 and Kendall correlation is achieved by a numerically soft formulation. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.

Contrary to non-metric 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 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

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


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