  There is a neglected fact in the traditional machine learning methods that
the data sampling can actually lead to the solution sampling. We consider this
observation to be important because having the solution sampling available
makes the variable distribution estimation, which is a problem in many
learning-related applications, more tractable. In this paper, we implement this
idea on correlation filter, which has attracted much attention in the past few
years due to its high performance with a low computational cost. More
specifically, we propose a new method, named latent constrained correlation
filters (LCCF) by mapping the correlation filters to a given latent subspace,
in which we establish a new learning framework that embeds distribution-related
constraints into the original problem. We further introduce a subspace based
alternating direction method of multipliers (SADMM) to efficiently solve the
optimization problem, which is proved to converge at the saddle point. Our
approach is successfully applied to two different tasks inclduing eye
localization and car detection. Extensive experiments demonstrate that LCCF
outperforms the state-of-the-art methods when samples are suffered from noise
and occlusion.
