Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this...
We describe an unsupervised learning algorithm for extracting sparse and locally shift-invariant features. We also devise a principled procedure for learning hierarchies of invari...
Abstract— Although the human hand is a complex biomechanical system, only a small set of features may be necessary for observation learning of functional grasp classes. We explor...
Lillian Y. Chang, Nancy S. Pollard, Tom M. Mitchel...
We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifier...
Antonio Torralba, Kevin P. Murphy, William T. Free...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...