A Soft k-Segments Algorithm for Principal Curves

Authors:
Jakob J. Verbeek;Nikos A. Vlassis;Ben J. A. Kröse
Affiliations:
-;-;-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 5
Cited 2

Growing cell structures—a self-organizing network for unsupervised and supervised learning

Neural Networks
GTM: the generative topographic mapping

Neural Computation
Mixtures of probabilistic principal component analyzers

Neural Computation
Learning and Design of Principal Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps

Self-Organizing Maps

Locally Defined Principal Curves and Surfaces

The Journal of Machine Learning Research
Contour-based shape representation using principal curves

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.