Project details for RegLin Posterior Regularization of Linear Mappings

Screenshot RegLin Posterior Regularization of Linear Mappings 1.0

by emstrick - November 9, 2011, 17:56:20 CET [ BibTeX BibTeX for corresponding Paper Download ]

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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

Other available revisons

Version Changelog Date
1.1

Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.

May 23, 2012, 10:31:34
1.0

Initial Announcement on mloss.org.

November 9, 2011, 17:56:20

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