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
Piecewise Linear Skeletonization Using Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
Principal curves with bounded turn
IEEE Transactions on Information Theory
Locally Defined Principal Curves and Surfaces
The Journal of Machine Learning Research
Real-time classification of dynamic hand gestures from marker-based position data
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
| Hi-index | 35.68 |
Time warping finds use in many fields of time series analysis, and it has been effectively implemented in many different application areas. Rather than focusing on a particular application area we approach the general problem definition, and employ principal curves, a powerful machine learning tool, to improve the noise robustness of existing time warping methods. The increasing noise level is the most important problem that leads to unnatural alignments. Therefore, we tested our approach in low signal-to-noise ratio (SNR) signals, and obtained satisfactory results. Moreover, for the signals denoised by principal curve projections we propose a differential equation-based time warping method, which has a comparable performance with lower computational complexity than the existing techniques.