Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Curvilinear Features in Spatial Point Patterns: Principal Curve Clustering with Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Another look at principal curves and surfaces
Journal of Multivariate Analysis
A k-segments algorithm for finding principal curves
Pattern Recognition Letters
Statistics and Computing
Extraction of curvilinear features from noisy point patterns using principal curves
Pattern Recognition Letters
IEEE Transactions on Signal Processing
A multifaceted perspective at data analysis: a study in collaborative intelligent agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Expert Systems with Applications: An International Journal
Locally Defined Principal Curves and Surfaces
The Journal of Machine Learning Research
Adaptive Constraint K-Segment Principal Curves for Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
Principal curves arising as an essential construct in dimensionality reduction and pattern recognition have recently attracted much attention from theoretical as well as practical perspective. Existing methods usually employ the first principal component of the data as an initial estimate of principal curves. However, they may be ineffective when dealing with complex data with self-intersecting characteristics, high curvature, and significant dispersion. In this paper, a new method based on global structure is proposed to detect the principal graph-a set of principal curves from complex data. First, the global structure of the data, called an initial principal graph, is extracted based on a thinning technique, which captures the approximate topological features of the complex data. In terms of the characteristics of the data, vertex-merge step and the improved fitting-and-smoothing phase are then proposed to control the deviation of the principal graph and improve the process of optimizing the principal graph. Finally, the restructuring step introduced by Kegl is used to rectify imperfections of the principal graph. By using synthetic and real-world data sets, the proposed method is compared with other existing algorithms. Experimental results show the effectiveness of the global structure based method.