A Family of Simple Non-Parametric Kernel Learning Algorithms from Pairwise Constraints

• Main Authors: ZHUANG, Jinfeng, TSANG, Ivor W., HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: non-parametric kernel learning; pairwise constraints; semi-definite programming; semi-supervised learning; side information; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2289; https://ink.library.smu.edu.sg/context/sis_research/article/3289/viewcontent/A_Family_of_Simple_Non_Parametric_Kernel_Learning_Algorithms_from_Pairwise_Constraints.pdf
• Institution: Singapore Management University

Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver co...