Sparseness and a reduction from Totally Nonnegative Least Squares to SVM

Vamsi K. Potluru, Sergey M. Plis, Shuang Luan, Vince D. Calhoun, Thomas P. Hayes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nonnegative Least Squares (NNLS) is a general form for many important problems. We consider a special case of NNLS where the input is nonnegative. It is called Totally Nonnegative Least Squares (TNNLS) in the literature. We show a reduction of TNNLS to a single class Support Vector Machine (SVM), thus relating the sparsity of a TNNLS solution to the sparsity of supports in a SVM. This allows us to apply any SVM solver to the TNNLS problem. We get an order of magnitude improvement in running time by first obtaining a smaller version of our original problem with the same solution using a fast approximate SVM solver. Second, we use an exact NNLS solver to obtain the solution. We present experimental evidence that this approach improves the performance of state-of-the-art NNLS solvers by applying it to both randomly generated problems as well as to real datasets, calculating radiation therapy dosages for cancer patients.

Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages1922-1929
Number of pages8
DOIs
StatePublished - Oct 24 2011
Externally publishedYes
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: Jul 31 2011Aug 5 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CountryUnited States
CitySan Jose, CA
Period7/31/118/5/11

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Potluru, V. K., Plis, S. M., Luan, S., Calhoun, V. D., & Hayes, T. P. (2011). Sparseness and a reduction from Totally Nonnegative Least Squares to SVM. In 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program (pp. 1922-1929). [6033459] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2011.6033459