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

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