Description:

Linear mappings are omnipresent in data processing analysis ranging from regression to distance metric learning. The interpretation of coefficients from under-determined mappings raises an unexpected challenge when the original modeling goal does not impose regularization. The RegLin package implements a general posterior regularization strategy for inducing unique results.

The benefits are: * reflection of data properties such as smoothness in spectrum profiles, easier interpretation of regularized mapping coefficients, and potentially improved mapping quality for unseen test data, i.e. better generalization.

The package also includes a function for standardizing the mapping coefficient vectors by projection to eigenvectors, and an attribute assessment strategy based on sensitivity analysis of the coefficient vectors - these two methods do not require under-determined systems.

The package contains example cases using pinv() and an external linear model (correlative matrix mapping CMM @ mloss.org) for colon cancer gene expression data and for a data base containing near-infrared spectral profiles. See Readme.txt contained in the package.

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: Regularization, Linear Model

Archive: download here

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