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

Affiliation: University of Geneva, Switzerland

ISBN: 978-989-674-011-5

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.

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Paper citation in several formats:
Szekely E.; Bruno E.; Marchand-Maillet S. and (2009). UNSUPERVISED DISCRIMINANT EMBEDDING IN CLUSTER SPACES.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, 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 - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={70-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002306500700076},
isbn={978-989-674-011-5},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - UNSUPERVISED DISCRIMINANT EMBEDDING IN CLUSTER SPACES
SN - 978-989-674-011-5
AU - Szekely, E.
AU - Bruno, E.
AU - Marchand-Maillet, S.
PY - 2009
SP - 70
EP - 76
DO - 10.5220/0002306500700076

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