Description:

Correlative Matrix Mapping (CMM): If X is a real-valued data matrix (row data vectors) and L the matrix with associated information (row 'label' vectors), then a linear mapping V is computed such that their distance matrices D_X^V and D_L, respectively, are mapped to provide maximum correlation r(D_X^V, D_L) = max. The matrix entries (D_X^V)_ij = sqrt( (x^i-x^j) * V * V' * (x^i-x^j) ) describe the adaptive (Mahalanobis-like) distance between data vectors x^i and x^j with V being optimized according to the maximum correlation mapping criterion induced by D_L.

Correlative Matrix Mapping (CMM) was formerly (before a naming conflict was recognized) known as Multivariate Subspace Regression (MSR) by Strickert, Soto, Vazquez (http://www.dice.ucl.ac.be/esann/proceedings/papers.php?ann=2010).

CMM supersedes Supervised Attribute Relevance Detection using Cross Comparisons (SARDUX) by Strickert, Soto, Vazquez (http://dig.ipk-gatersleben.de/sardux/sardux.html)

The CMM approach is related to canonical correlation analysis, but transforms only the data space to match the well-known static 'label' 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: Lda, Dimensionality Reduction, Supervised Learning, Linear Discriminant Analysis, Association Mapping, Canonical Correlation Analysis, Cca, Linear Model

Archive: download here

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