loading
Documents

Research.Publish.Connect.

Paper

Author: Oliver Kramer

Affiliation: University of Oldenburg, Germany

ISBN: 978-989-758-276-9

Keyword(s): Dimensionality Reduction, Iterative Learning, Evolutionary Optimization, Shephard-Kruskal Measure.

Related Ontology Subjects/Areas/Topics: Embedding and Manifold Learning ; Evolutionary Computation ; Pattern Recognition ; Theory and Methods

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.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.85.143.239

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.