Unsupervised and Semisupervised Classification Via Absolute Value Inequalities

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• 作者：Glenn M. Fung ; Olvi L. Mangasarian
• 关键词：Unsupervised classification ; Absolute value inequalities ; Support vector machines ; Data analysis ; Learning theory ; Linear programming ; Linear inequalities
• 刊名：Journal of Optimization Theory and Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：168
• 期：2
• 页码：551-558
• 全文大小：374 KB
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• 作者单位：Glenn M. Fung (1)
  Olvi L. Mangasarian (2) (3)
  
  1. Business and Customer Operations Unit, American Family Insurance, Madison, WI, 53783, USA
  2. Computer Sciences Department, University of Wisconsin, Madison, WI, 53706, USA
  3. Department of Mathematics, University of California at San Diego, La Jolla, CA, 92093, USA
  
• 刊物主题：Calculus of Variations and Optimal Control; Optimization; Optimization; Theory of Computation; Applications of Mathematics; Engineering, general; Operations Research/Decision Theory;
• 出版者：Springer US
• ISSN：1573-2878

文摘

We consider the problem of classifying completely or partially unlabeled data by using inequalities that contain absolute values of the data. This allows each data point to belong to either one of two classes by entering the inequality with a plus or minus value. By using such absolute value inequalities in linear and nonlinear support vector machines, unlabeled or partially labeled data can be successfully partitioned into two classes that capture most of the correct labels dropped from the unlabeled data. Keywords Unsupervised classification Absolute value inequalities Support vector machines