Authors:
Maximilian Münch
1
;
2
;
Christoph Raab
3
;
1
;
Michael Biehl
2
and
Frank-Michael Schleif
1
Affiliations:
1
Department of Computer Science and Business Information Systems,University of Applied Sciences Würzburg-Schweinfurt, D-97074 Würzburg, Germany
;
2
University of Groningen, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, P.O. Box 407, NL-9700 AK Groningen, The Netherlands
;
3
Bielefeld University, Center of Excellence, Cognitive Interaction Technology, CITEC, D-33619 Bielefeld, Germany
Keyword(s):
Non-euclidean, Similarity, Indefinite, Von Mises Iteration, Eigenvalue Correction, Shifting, Flipping, Clipping.
Abstract:
Domain-specific proximity measures, like divergence measures in signal processing or alignment scores in bioinformatics, often lead to non-metric, indefinite similarities or dissimilarities. However, many classical learning algorithms like kernel machines assume metric properties and struggle with such metric violations. For example, the classical support vector machine is no longer able to converge to an optimum. One possible direction to solve the indefiniteness problem is to transform the non-metric (dis-)similarity data into positive (semi-)definite matrices. For this purpose, many approaches have been proposed that adapt the eigenspectrum of the given data such that positive definiteness is ensured. Unfortunately, most of these approaches modify the eigenspectrum in such a strong manner that valuable information is removed or noise is added to the data. In particular, the shift operation has attracted a lot of interest in the past few years despite its frequently reoccurring dis
advantages. In this work, we propose a modified advanced shift correction method that enables the preservation of the eigenspectrum structure of the data by means of a low-rank approximated nullspace correction. We compare our advanced shift to classical eigenvalue corrections like eigenvalue clipping, flipping, squaring, and shifting on several benchmark data. The impact of a low-rank approximation on the data’s eigenspectrum is analyzed.
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