loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Eniko Szekely ; Eric Bruno and Stephane Marchand-Maillet

Affiliation: University of Geneva, Switzerland

Keyword(s): Dimension reduction, Clustering, High dimensionality.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Reduction and Quality Assessment ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining High-Dimensional Data ; Pre-Processing and Post-Processing for Data Mining ; Symbolic Systems ; Visual Data Mining and Data Visualization

Abstract: This paper proposes a new representation space, called the cluster space, for data points that originate from high dimensions. Whereas existing dedicated methods concentrate on revealing manifolds from within the data, we consider here the context of clustered data and derive the dimension reduction process from cluster information. Points are represented in the cluster space by means of their a posteriori probability values estimated using Gaussian Mixture Models. The cluster space obtained is the optimal space for discrimination in terms of the Quadratic Discriminant Analysis (QDA).Moreover, it is shown to alleviate the negative impact of the curse of dimensionality on the quality of cluster discrimination and is a useful preprocessing tool for other dimension reduction methods. Various experiments illustrate the effectiveness of the cluster space both on synthetic and real data.

CC BY-NC-ND 4.0

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 3.143.9.115

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:
Szekely, E.; Bruno, E. and Marchand-Maillet, S. (2009). UNSUPERVISED DISCRIMINANT EMBEDDING IN CLUSTER SPACES. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2009) - KDIR; ISBN 978-989-674-011-5; ISSN 2184-3228, SciTePress, pages 70-76. DOI: 10.5220/0002306500700076

@conference{kdir09,
author={Eniko Szekely. and Eric Bruno. and Stephane Marchand{-}Maillet.},
title={UNSUPERVISED DISCRIMINANT EMBEDDING IN CLUSTER SPACES},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2009) - KDIR},
year={2009},
pages={70-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002306500700076},
isbn={978-989-674-011-5},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2009) - KDIR
TI - UNSUPERVISED DISCRIMINANT EMBEDDING IN CLUSTER SPACES
SN - 978-989-674-011-5
IS - 2184-3228
AU - Szekely, E.
AU - Bruno, E.
AU - Marchand-Maillet, S.
PY - 2009
SP - 70
EP - 76
DO - 10.5220/0002306500700076
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic