Supervised low rank indefinite kernel approximation using minimum enclosing balls

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Supervised low rank indefinite kernel approximation using minimum enclosing balls. / Schleif, Frank Michael; Gisbrecht, Andrej; Tino, Peter.

julkaisussa: Neurocomputing, Vuosikerta 318, 27.11.2018, s. 213-226.

Tutkimustuotos: Lehtiartikkeli

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Schleif, Frank Michael ; Gisbrecht, Andrej ; Tino, Peter. / Supervised low rank indefinite kernel approximation using minimum enclosing balls. Julkaisussa: Neurocomputing. 2018 ; Vuosikerta 318. Sivut 213-226.

Bibtex - Lataa

@article{2089734549a046548ac6fbcf79df2843,
title = "Supervised low rank indefinite kernel approximation using minimum enclosing balls",
abstract = "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 Nystr{\"o}m 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.",
keywords = "Classification, Indefinite kernel, Indefinite learning, Kernel fisher discriminant, Low rank approximation, Minimum enclosing ball, Nystr{\"o}m approximation",
author = "Schleif, {Frank Michael} and Andrej Gisbrecht and Peter Tino",
year = "2018",
month = "11",
day = "27",
doi = "10.1016/j.neucom.2018.08.057",
language = "English",
volume = "318",
pages = "213--226",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier BV",

}

RIS - Lataa

TY - JOUR

T1 - Supervised low rank indefinite kernel approximation using minimum enclosing balls

AU - Schleif, Frank Michael

AU - Gisbrecht, Andrej

AU - Tino, Peter

PY - 2018/11/27

Y1 - 2018/11/27

N2 - 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 Nyström 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.

AB - 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 Nyström 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.

KW - Classification

KW - Indefinite kernel

KW - Indefinite learning

KW - Kernel fisher discriminant

KW - Low rank approximation

KW - Minimum enclosing ball

KW - Nyström approximation

UR - http://www.scopus.com/inward/record.url?scp=85053037192&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2018.08.057

DO - 10.1016/j.neucom.2018.08.057

M3 - Article

VL - 318

SP - 213

EP - 226

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -

ID: 28340899