Learning relative similarity by stochastic dual coordinate ascent

• المؤلفون الرئيسيون: WU, Pengcheng, YI, Ding, ZHAO, Peilin, MIAO, Chunyan, HOI, Steven C. H.
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2014
• الموضوعات: distance metric learning; similarity learning; online learning; retrieval; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/sis_research/2321; https://ink.library.smu.edu.sg/context/sis_research/article/3321/viewcontent/8415_38543_1_PB.pdf
• المؤسسة: Singapore Management University

Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper,...