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- W2963640279 abstract "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 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." @default.
- W2963640279 created "2019-07-30" @default.
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- W2963640279 date "2017-10-19" @default.
- W2963640279 modified "2023-09-26" @default.
- W2963640279 title "Indefinite Kernel Logistic Regression" @default.
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- W2963640279 doi "https://doi.org/10.1145/3123266.3123295" @default.
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