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Distance algorithm based procedure for non‐negative least squares
Author(s) -
Rajkó Róbert,
Zheng Yu
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2625
Subject(s) - algorithm , least squares function approximation , chemometrics , task (project management) , computer science , visualization , mathematics , artificial intelligence , statistics , machine learning , management , estimator , economics
In chemistry and many other scientific disciplines, non‐negativity‐constrained estimation of models is of practical importance. The time required for estimating true least squares non‐negativity‐constrained models is typically many times longer than that for estimating unconstrained models. That is why it is necessary to find faster and faster non‐negative least squares (NNLS) algorithms. Very recently, the distance algorithm has been developed, and this algorithm can be adapted to solve NNLS regression task faster (in some cases) than the conventional algorithms. Based on some simulated investigation, DA_NNLS was the fastest for small‐sized and medium‐sized linear regression tasks. The visualization (geometry) of the NNLS task being solved by our new algorithm is discussed as well. Besides linear algebra, convex geometrical concepts and tools are suggested to investigate, to use, and to develop in chemometrics for exploiting the geometry of chemometry. Copyright © 2014 John Wiley & Sons, Ltd.