1-norm support vector novelty detection and its sparseness.
basic_science · Level V
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- Record sourced from PubMed, PMID 23994511.
- Also identified by DOI 10.1016/j.neunet.2013.07.010.
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Abstract
This paper proposes a 1-norm support vector novelty detection (SVND) method and discusses its sparseness. 1-norm SVND is formulated as a linear programming problem and uses two techniques for inducing sparseness, or the 1-norm regularization and the hinge loss function. We also find two upper bounds on the sparseness of 1-norm SVND, or exact support vector (ESV) and kernel Gram matrix rank bounds. The ESV bound indicates that 1-norm SVND has a sparser representation model than SVND. The kernel Gram matrix rank bound can loosely estimate the sparseness of 1-norm SVND. Experimental results show that 1-norm SVND is feasible and effective.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Programming, Linear
- Support Vector Machine