Principal curve tracing

We propose a principal curve tracing algorithm that uses the gradient and the Hessian of a given density estimate. Curve definition requires the local smoothness of data density and is based on the concept of subspace local maxima. Tracing of the curve is handled through the leading eigenvector where fixed-step updates are used. We also propose an image segmentation algorithm based on the original idea and show the effectiveness of the proposed algorithm on a Brainbow dataset.

[1]  Adam Krzyzak,et al.  Learning and Design of Principal Curves , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Deniz Erdogmus,et al.  Local conditions for critical and principal manifolds , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  T. Hastie,et al.  Principal Curves , 2007 .

[4]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Deniz Erdogmus,et al.  Self-Consistent Locally Defined Principal Surfaces , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[6]  Gerhard Tutz,et al.  Local principal curves , 2005, Stat. Comput..

[7]  R. Tibshirani Principal curves revisited , 1992 .

[8]  Sanjeev R. Kulkarni,et al.  Principal curves with bounded turn , 2002, IEEE Trans. Inf. Theory.