Multi-view EM algorithm and its application to color image segmentation
We propose a new algorithm, the multi-view expectation and maximization algorithm (multi-view EM), to deal with real-world learning problems where there are some natural split of features. Multi-view EM does feature split in the same manner as co-training and co-EM, two successful semi-supervised learning algorithms in text learning tasks, but it considers the multi-view learning problem in the framework of the EM algorithm. The multi-view EM algorithm has impressive advantages compared with co-training and co-EM: its convergence is theoretically guaranteed; and it can deal with multiple views instead of only two views. We utilize it for color image segmentation and discuss the phenomenon that different weights for the color view and coordinate view lead to different segmentation results.