Auto-adaptive Laplacian Pyramids

Ángela Fernández; Neta Rabin; Dalia Fishelov; José R. Dorronsoro

Auto-adaptive Laplacian Pyramids

Ángela Fernández, Neta Rabin, Dalia Fishelov, José R. Dorronsoro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

An important challenge in Data Mining and Machine Learning is the proper analysis of a given dataset, especially for understanding and working with functions defined over it. In this paper we propose Auto-adaptive Laplacian Pyramids (ALP) for target function smoothing when the target function is defined on a high-dimensional dataset. The proposed algorithm automatically selects the optimal function resolution (stopping time) adapted to the data defined and its noise. We illustrate its application on a radiation forecasting example.

Original language	English
Title of host publication	ESANN 2016 - 24th European Symposium on Artificial Neural Networks
Pages	59-64
Number of pages	6
ISBN (Electronic)	9782875870278
State	Published - 2016
Externally published	Yes
Event	24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgium Duration: 27 Apr 2016 → 29 Apr 2016

Publication series

Name	ESANN 2016 - 24th European Symposium on Artificial Neural Networks

Conference

Conference	24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016
Country/Territory	Belgium
City	Bruges
Period	27/04/16 → 29/04/16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems

Cite this

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title = "Auto-adaptive Laplacian Pyramids",

abstract = "An important challenge in Data Mining and Machine Learning is the proper analysis of a given dataset, especially for understanding and working with functions defined over it. In this paper we propose Auto-adaptive Laplacian Pyramids (ALP) for target function smoothing when the target function is defined on a high-dimensional dataset. The proposed algorithm automatically selects the optimal function resolution (stopping time) adapted to the data defined and its noise. We illustrate its application on a radiation forecasting example.",

author = "{\'A}ngela Fern{\'a}ndez and Neta Rabin and Dalia Fishelov and Dorronsoro, {Jos{\'e} R.}",

year = "2016",

language = "الإنجليزيّة",

series = "ESANN 2016 - 24th European Symposium on Artificial Neural Networks",

pages = "59--64",

booktitle = "ESANN 2016 - 24th European Symposium on Artificial Neural Networks",

note = "24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 ; Conference date: 27-04-2016 Through 29-04-2016",

}

Fernández, Á, Rabin, N, Fishelov, D & Dorronsoro, JR 2016, Auto-adaptive Laplacian Pyramids. in ESANN 2016 - 24th European Symposium on Artificial Neural Networks. ESANN 2016 - 24th European Symposium on Artificial Neural Networks, pp. 59-64, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016, Bruges, Belgium, 27/04/16.

TY - GEN

T1 - Auto-adaptive Laplacian Pyramids

AU - Fernández, Ángela

AU - Rabin, Neta

AU - Fishelov, Dalia

AU - Dorronsoro, José R.

PY - 2016

Y1 - 2016

N2 - An important challenge in Data Mining and Machine Learning is the proper analysis of a given dataset, especially for understanding and working with functions defined over it. In this paper we propose Auto-adaptive Laplacian Pyramids (ALP) for target function smoothing when the target function is defined on a high-dimensional dataset. The proposed algorithm automatically selects the optimal function resolution (stopping time) adapted to the data defined and its noise. We illustrate its application on a radiation forecasting example.

AB - An important challenge in Data Mining and Machine Learning is the proper analysis of a given dataset, especially for understanding and working with functions defined over it. In this paper we propose Auto-adaptive Laplacian Pyramids (ALP) for target function smoothing when the target function is defined on a high-dimensional dataset. The proposed algorithm automatically selects the optimal function resolution (stopping time) adapted to the data defined and its noise. We illustrate its application on a radiation forecasting example.

UR - http://www.scopus.com/inward/record.url?scp=84994176873&partnerID=8YFLogxK

M3 - منشور من مؤتمر

T3 - ESANN 2016 - 24th European Symposium on Artificial Neural Networks

SP - 59

EP - 64

BT - ESANN 2016 - 24th European Symposium on Artificial Neural Networks

T2 - 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016

Y2 - 27 April 2016 through 29 April 2016

ER -

Auto-adaptive Laplacian Pyramids

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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Cite this