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A Matlab Toolbox for Sparse Statistical Modeling
Abstract
Applications in biotechnology such as gene expression analysis and image processing
have led to a tremendous development of statistical methods with emphasis on reliable
solutions to severely underdetermined systems, and interpretation, solutions where the
surplus of inputs have been reduced to a concise model. At the core of this development
are methods which augments the standard linear models for regression, classification and
decomposition such that sparse solutions are obtained. This toolbox aims at making
public carefully implemented and well-tested variants of the most popular such methods
for the Matlab programming environment. The toolbox builds on code made public in
2005 and which has since been used in several studies.
The toolbox consists of a series of Matlab (The MathWorks Inc. 201
matlab
统计
稀疏
建模
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