Multi-task learning with one-class SVM

• Published in: Neurocomputing (Amsterdam) Vol. 133; pp. 416 - 426
• Main Authors: He, Xiyan, Mourot, Gilles, Maquin, Didier, Ragot, José, Beauseroy, Pierre, Smolarz, André, Grall-Maës, Edith
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 10.06.2014; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Automatic; Computer science; control theory; systems; Design optimization; Diagnostics; Engineering Sciences; Exact sciences and technology; Kernels; Learning; Learning and adaptive systems; Machine learning; Mathematical models; Multi-task learning; Multiple sources; Nonlinearity; One-class SVM; Optimization; Pattern recognition. Digital image processing. Computational geometry; Programming theory; Support vector machines; Tasks; Theoretical computing; One-class SVM; Multiple sources; Diagnostics; Multi-task learning; Machine learning; Computer vision; Data analysis; Program optimization; Generalization; Inductive learning; Modeling; Texture; Knowledge transfer; One-class classification; Kernel method; Image segmentation; Multidimensional analysis; Multithread; Multiplicity; Vector support machine; Learning algorithm; Artificial intelligence; Binary classification; multi-task learning; diagnostics; multiple sources; one-class SVM
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.12.022

Multi-task learning technologies have been developed to be an effective way to improve the generalization performance by training multiple related tasks simultaneously. The determination of the relatedness between tasks is usually the key to the formulation of a multi-task learning method. In this paper, we make the assumption that when tasks are related to each other, usually their models are close enough, that is, their models or their model parameters are close to a certain mean function. Following this task relatedness assumption, two multi-task learning formulations based on one-class support vector machines (one-class SVM) are presented. With the help of new kernel design, both multi-task learning methods can be solved by the optimization program of a single one-class SVM. Experiments conducted on both low-dimensional nonlinear toy dataset and high-dimensional textured images show that our approaches lead to very encouraging results.