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Article

IDEA: intrinsic dimension estimation algorithm

Authors:

Alessandro Rozza,

Gabriele Lombardi,

Elena Casiraghi,

Paola CampadelliAuthors Info & Claims

ICIAP'11: Proceedings of the 16th international conference on Image analysis and processing: Part I

Pages 433 - 442

Published: 14 September 2011 Publication History

Abstract

The high dimensionality of some real life signals makes the usage of the most common signal processing and pattern recognition methods unfeasible. For this reason, in literature a great deal of research work has been devoted to the development of algorithms performing dimensionality reduction. To this aim, an useful help could be provided by the estimation of the intrinsic dimensionality of a given dataset, that is the minimum number of parameters needed to capture, and describe, all the information carried by the data. Although many techniques have been proposed, most of them fail in case of noisy data or when the intrinsic dimensionality is too high. In this paper we propose a local intrinsic dimension estimator exploiting the statistical properties of data neighborhoods. The algorithm evaluation on both synthetic and real datasets, and the comparison with state of the art algorithms, proves that the proposed technique is promising.

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Cited By

Kuleshov ABernstein A(2016)Extended Regression on Manifolds EstimationProceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications - Volume 965310.1007/978-3-319-33395-3_15(208-228)Online publication date: 20-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-33395-3_15
Bassis SRozza ACeruti CLombardi GCasiraghi ECampadelli P(2012)A Novel Intrinsic Dimensionality Estimator Based on Rank-Order StatisticsRevised Selected Papers of the First International Workshop on Clustering High--Dimensional Data - Volume 762710.1007/978-3-662-48577-4_7(102-117)Online publication date: 15-May-2012
https://dl.acm.org/doi/10.1007/978-3-662-48577-4_7
Camastra F(2012)Data Dimensionality EstimationRevised Selected Papers of the First International Workshop on Clustering High--Dimensional Data - Volume 762710.1007/978-3-662-48577-4_6(87-101)Online publication date: 15-May-2012
https://dl.acm.org/doi/10.1007/978-3-662-48577-4_6

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ICIAP'11: Proceedings of the 16th international conference on Image analysis and processing: Part I

September 2011

709 pages

ISBN:9783642240843

Editors:
Giuseppe Maino
Università di Bologna, Facoltà di Conservazione dei Beni Culturali, Ravenna, Italy
,
Gian Luca Foresti
Università di Udine, Dipartimento di Matematica e Informatica, Udine, Italy

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 September 2011

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Cited By

Kuleshov ABernstein A(2016)Extended Regression on Manifolds EstimationProceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications - Volume 965310.1007/978-3-319-33395-3_15(208-228)Online publication date: 20-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-33395-3_15
Bassis SRozza ACeruti CLombardi GCasiraghi ECampadelli P(2012)A Novel Intrinsic Dimensionality Estimator Based on Rank-Order StatisticsRevised Selected Papers of the First International Workshop on Clustering High--Dimensional Data - Volume 762710.1007/978-3-662-48577-4_7(102-117)Online publication date: 15-May-2012
https://dl.acm.org/doi/10.1007/978-3-662-48577-4_7
Camastra F(2012)Data Dimensionality EstimationRevised Selected Papers of the First International Workshop on Clustering High--Dimensional Data - Volume 762710.1007/978-3-662-48577-4_6(87-101)Online publication date: 15-May-2012
https://dl.acm.org/doi/10.1007/978-3-662-48577-4_6

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