Indefinite Kernel Logistic Regression

Fanghui Liu, Xiaolin Huang, Jie Yang
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
Traditionally, kernel learning methods require positive definitiveness on the kernel, which is too strict and excludes many sophisticated similarities, that are indefinite. To utilize those indefinite kernels, indefinite learning methods are of great interests. This paper aims at the extension of the logistic regression from positive definite kernels to indefinite ones. The proposed model, named indefinite kernel logistic regression (IKLR), keeps consistency to the regular KLR in formulation
more » ... R in formulation but it essentially becomes non-convex. Thanks to the positive decomposition of an indefinite kernel, IKLR can be transformed into a difference of two convex models, which follows the use of concave-convex procedure. Moreover, aiming at large-scale problems in practice, a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme is proposed with convergence guarantees. Experimental results on multi-modal datasets demonstrate the superiority of the proposed IKLR model over kernel logistic regression with positive definite kernels and other state-of-the-art indefinite learning based methods.
doi:10.1145/3123266.3123295 dblp:conf/mm/LiuHY17 fatcat:fvofrgswtvfovbsv3gvochoy4m