An empirical study on the performance of spectral manifold learning techniques

Peter Mysling; Søren Hauberg; Kim Steenstrup Pedersen

doi:10.1007/978-3-642-21735-7_43

An empirical study on the performance of spectral manifold learning techniques

Peter Mysling, Søren Hauberg, Kim Steenstrup Pedersen

Datalogisk Institut

3 Citationer (Scopus)

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.

Originalsprog	Engelsk
Titel	Artificial Neural Networks and Machine Learning – ICANN 2011 : 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I
Redaktører	Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski
Antal sider	8
Forlag	Springer
Publikationsdato	2011
Sider	347-354
ISBN (Trykt)	978-3-642-21734-0
ISBN (Elektronisk)	978-3-642-21735
DOI	https://doi.org/10.1007/978-3-642-21735-7_43
Status	Udgivet - 2011
Begivenhed	21st International Conference on Artificial Neural Networks - Espoo, Finland Varighed: 14 jun. 2011 → 17 jun. 2011 Konferencens nummer: 21

Konference

Konference	21st International Conference on Artificial Neural Networks
Nummer	21
Land/Område	Finland
By	Espoo
Periode	14/06/2011 → 17/06/2011

Navn	Lecture notes in computer science
Vol/bind	6791
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-642-21735-7_43

Citationsformater

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_43

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 › peer review

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_43

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). doi: 10.1007/978-3-642-21735-7_43

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).

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