Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings

Oliver Kramer

Abstract

This paper introduces an evolutionary iterative approximation of Shephard-Kruskal based dimensionality reduction with linear runtime. The method, which we call evolutionary Shephard-Kruskal embedding (EvoSK), iteratively constructs a low-dimensional representation with Gaussian sampling in the environment of the latent positions of the closest embedded patterns. The approach explicitly optimizes the distance preservation in low-dimensional space, similar to the objective solved by multi-dimensional scaling. Experiments on a small benchmark data set show that EvoSK can perform better than its famous counterparts multi-dimensional scaling and isometric mapping and outperforms stochastic neighbor embeddings.

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Paper Citation


in Harvard Style

Kramer O. (2018). Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 478-481. DOI: 10.5220/0006645904780481


in Bibtex Style

@conference{icpram18,
author={Oliver Kramer},
title={Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={478-481},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006645904780481},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings
SN - 978-989-758-276-9
AU - Kramer O.
PY - 2018
SP - 478
EP - 481
DO - 10.5220/0006645904780481