Abstract

AbstractIndefinite similarity measures can be frequently found in bio-informatics by means of alignment scores. Lacking an underlying vector space, the data are given as pairwise similarities only. Indefinite Kernel Fisher Discriminant (iKFD) is a very effective classifier for this type of data but has cubic complexity and does not scale to larger problems. Here we propose an extension of iKFD such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Evaluation at several larger similarity data from various domains shows that the proposed method provides similar generalization capabilities while being substantially faster for large scale data.KeywordsIndefinite KernelsKernel Fisher Discriminant AnalysisApproximate Kernel MatrixSparse Parameter VectorUnderlying Data SpaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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