Supervised low rank indefinite kernel approximation using minimum enclosing balls

NEUROCOMPUTING

卷:318

Article

Schleif, Frank-Michael^1,3  Gisbrecht, Andrej²  Tino, Peter¹ 

[1] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England

[2] Aalto Univ, Dept Comp Sci, Helsinki Inst Informat Technol, Espoo, Finland

[3] Univ Appl Sc Wuerzburg Schweinfurt, Sch Comp Sci, D-97074 Wurzburg, Germany

关键词: Indefinite kernel;    Kernel fisher discriminant;    Minimum enclosing ball;    Nystrom approximation;    Low rank approximation;    Classification;    Indefinite learning;   

DOI  :  10.1016/j.neucom.2018.08.057

来源: Elsevier

【 摘 要 】

Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval. Lacking an underlying vector space, the data are given as pairwise similarities only. The few algorithms available for such data do not scale to larger datasets. Focusing on probabilistic batch classifiers, the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic Classification Vector Machine (PCVM) are both effective algorithms for this type of data but, with cubic complexity. Here we propose an extension of iKFD and PCVM such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Employing the Nystrom approximation for indefinite kernels, we also propose a new almost parameter free approach to identify the landmarks, restricted to a supervised learning problem. Evaluations at several larger similarity data from various domains show that the proposed methods provides similar generalization capabilities while being easier to parametrize and substantially faster for large scale data. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free