Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings

Oliver Kramer

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings

Topics: Embedding and Manifold Learning; Evolutionary Computation

In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 478-481, 2018 , Funchal, Madeira, Portugal

Author: Oliver Kramer

Affiliation: University of Oldenburg, Germany

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.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.145.10.68

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 - ICPRAM; ISBN 978-989-758-276-9; ISSN 2184-4313, SciTePress, 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 - ICPRAM},
year={2018},
pages={478-481},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006645904780481},
isbn={978-989-758-276-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings
SN - 978-989-758-276-9
IS - 2184-4313
AU - Kramer, O.
PY - 2018
SP - 478
EP - 481
DO - 10.5220/0006645904780481
PB - SciTePress