Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...