Structure Preserving Encoding of Non-euclidean Similarity Data

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif

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 disadvantages. 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.

Download


Paper Citation


in Harvard Style

Münch M., Raab C., Biehl M. and Schleif F. (2020). Structure Preserving Encoding of Non-euclidean Similarity Data.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 43-51. DOI: 10.5220/0008955100430051


in Bibtex Style

@conference{icpram20,
author={Maximilian Münch and Christoph Raab and Michael Biehl and Frank-Michael Schleif},
title={Structure Preserving Encoding of Non-euclidean Similarity Data},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={43-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008955100430051},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Structure Preserving Encoding of Non-euclidean Similarity Data
SN - 978-989-758-397-1
AU - Münch M.
AU - Raab C.
AU - Biehl M.
AU - Schleif F.
PY - 2020
SP - 43
EP - 51
DO - 10.5220/0008955100430051