A soft k-segments algorithm for principal curves

Verbeek, J. J.; Vlassis, Nikos; Krose, B.

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A soft k-segments algorithm for principal curves

Verbeek, J. J.; Vlassis, Nikos; Krose, B.

2001 • In ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS

Peer reviewed

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https://hdl.handle.net/10993/11079

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Abstract :

[en] We propose a new method to find principal curves for data sets. The method repeats three steps until a stopping criterion is met. In the first step, k (unconnected) line segments are fitted on the data. The second step connects the segments to form a polygonal line, and evaluates the quality of the resulting polygonal line. The third step inserts a new line segment. We compare the performance of our new method with other existing methods to find principal curves.

Disciplines :

Computer science

Identifiers :

UNILU:UL-ARTICLE-2011-741

Author, co-author :

Verbeek, J. J.

Vlassis, Nikos ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)

Krose, B.

Language :

English

Title :

A soft k-segments algorithm for principal curves

Publication date :

2001

Event name :

ARTIFICIAL NEURAL NETWORKS-ICANN 2001

Event date :

2001

Main work title :

ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS

Pages :

450-456

Peer reviewed :

Peer reviewed

Available on ORBilu :

since 17 November 2013

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Bibliography

C. M. Bishop, M. Svensén, and C. K. I Williams. GTM: The generative topographic mapping. Neural Computation, 10:215–234, 1998.
B. Fritzke. Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Networks, 7(9):1441–1460, 1994.
T. Hastie and W. Stuetzle. Principal curves. Journal of the American Statistical Association, 84(406):502–516, 1989.
I.T. Jolliffe. Principal Component Analysis. Springer-Verlag, New York, 1986.
B. Kégl, A. Krzyzak, T. Linder, and K. Zeger. Learning and design of principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(3):281–297, 2000.
T. Kohonen. Self-Organizing Maps. Springer-Verlag, 1995.
E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, and D.B. Shmoys, editors. The Traveling Salesman Problem. Series in Discrete Mathematics and Optimization. John Wiley & Sons, 1985.
J. Q. Li and A. R. Barron. Mixture density estimation. In Advances in Neural Information Processing Systems 12. The MIT Press, 2000.
J.B. Tenenbaum, V. de Silva, and J.C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319–2323, December 2000.
R. Tibshirani. Principal curves revisited. Statistics and Computing, 2:183–190, 1992.
M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analysers. Neural Computation, 11(2):443–482, 1999.
J.J. Verbeek, N. Vlassis, and B. Kröse. A k-segments algorithm to find principal curves. Technical Report IAS-UVA-00-11, Computer Science Institute, University of Amsterdam, 2000.