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An empirical study on the performance of spectral manifold learning techniques. / Mysling, Peter; Hauberg, Søren; Pedersen, Kim Steenstrup.
Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. red. / Timo Honkela; Włodzisław Duch; Mark Girolami; Samuel Kaski. Springer, 2011. s. 347-354 (Lecture notes in computer science, Bind 6791).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Mysling, P, Hauberg, S
& Pedersen, KS 2011,
An empirical study on the performance of spectral manifold learning techniques. i T Honkela, W Duch, M Girolami & S Kaski (red),
Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. Springer, Lecture notes in computer science, bind 6791, s. 347-354, 21st International Conference on Artificial Neural Networks, Espoo, Finland,
14/06/2011.
https://doi.org/10.1007/978-3-642-21735-7_43APA
Mysling, P., Hauberg, S.
, & Pedersen, K. S. (2011).
An empirical study on the performance of spectral manifold learning techniques. I T. Honkela, W. Duch, M. Girolami, & S. Kaski (red.),
Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I (s. 347-354). Springer. Lecture notes in computer science Bind 6791
https://doi.org/10.1007/978-3-642-21735-7_43Vancouver
Mysling P, Hauberg S
, Pedersen KS.
An empirical study on the performance of spectral manifold learning techniques. I Honkela T, Duch W, Girolami M, Kaski S, red., Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. Springer. 2011. s. 347-354. (Lecture notes in computer science, Bind 6791).
https://doi.org/10.1007/978-3-642-21735-7_43Author
Mysling, Peter ; Hauberg, Søren ; Pedersen, Kim Steenstrup. / An empirical study on the performance of spectral manifold learning techniques. Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. red. / Timo Honkela ; Włodzisław Duch ; Mark Girolami ; Samuel Kaski. Springer, 2011. s. 347-354 (Lecture notes in computer science, Bind 6791).
Bibtex
@inproceedings{9773d89a74d04acf9d6731832273a0bb,
title = "An empirical study on the performance of spectral manifold learning techniques",
abstract = "In recent years, there has been a surge of interest in spectral manifold learning techniques. Despite the interest, only little work has focused on the empirical behavior of these techniques. We construct synthetic data of variable complexity and observe the performance of the techniques as they are subjected to increasingly difficult problems. We evaluate performance in terms of both a classification and a regression task. Our study includes Isomap, LLE, Laplacian eigenmaps, and diffusion maps. Among others, our results indicate that the techniques are highly dependent on data density, sensitive to scaling, and greatly influenced by intrinsic dimensionality.",
author = "Peter Mysling and S{\o}ren Hauberg and Pedersen, {Kim Steenstrup}",
year = "2011",
doi = "10.1007/978-3-642-21735-7_43",
language = "English",
isbn = "978-3-642-21734-0",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "347--354",
editor = "Timo Honkela and Duch, { W{\l}odzis{\l}aw} and Mark Girolami and Samuel Kaski",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2011",
address = "Switzerland",
note = "null ; Conference date: 14-06-2011 Through 17-06-2011",
}
RIS
TY - GEN
T1 - An empirical study on the performance of spectral manifold learning techniques
AU - Mysling, Peter
AU - Hauberg, Søren
AU - Pedersen, Kim Steenstrup
N1 - Conference code: 21
PY - 2011
Y1 - 2011
N2 - In recent years, there has been a surge of interest in spectral
manifold learning techniques. Despite the interest, only little work
has focused on the empirical behavior of these techniques. We construct
synthetic data of variable complexity and observe the performance of
the techniques as they are subjected to increasingly difficult problems.
We evaluate performance in terms of both a classification and a regression
task. Our study includes Isomap, LLE, Laplacian eigenmaps, and
diffusion maps. Among others, our results indicate that the techniques
are highly dependent on data density, sensitive to scaling, and greatly
influenced by intrinsic dimensionality.
AB - In recent years, there has been a surge of interest in spectral
manifold learning techniques. Despite the interest, only little work
has focused on the empirical behavior of these techniques. We construct
synthetic data of variable complexity and observe the performance of
the techniques as they are subjected to increasingly difficult problems.
We evaluate performance in terms of both a classification and a regression
task. Our study includes Isomap, LLE, Laplacian eigenmaps, and
diffusion maps. Among others, our results indicate that the techniques
are highly dependent on data density, sensitive to scaling, and greatly
influenced by intrinsic dimensionality.
U2 - 10.1007/978-3-642-21735-7_43
DO - 10.1007/978-3-642-21735-7_43
M3 - Article in proceedings
SN - 978-3-642-21734-0
T3 - Lecture notes in computer science
SP - 347
EP - 354
BT - Artificial Neural Networks and Machine Learning – ICANN 2011
A2 - Honkela, Timo
A2 - Duch, Włodzisław
A2 - Girolami, Mark
A2 - Kaski, Samuel
PB - Springer
Y2 - 14 June 2011 through 17 June 2011
ER -
