# 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.

Download#### 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