title: 1-norm support vector novelty detection and its sparseness.
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.
authors.affiliation: Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China. Electronic address: lizhang.ml@gmail.com.
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.forename: WeiDa
authors.initials: W
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D011382:Programming, Linear; D060388:Support Vector Machine; D014920:Wine / Q000145:classification
keywords: 1-norm regularization; Linear programming; Support vector novelty detection