1 research outputs found
Probabilistic classifiers with low rank indefinite kernels
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\"om 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