Find the following information about this article:
Title: Large scale metric learning from equivalence constraints
Author: Kostinger, Hirzer, Wohlhart, Roth, Bischof
Journal/Conference: CVPR
Year: 2012
 
- Different distance measure ?
- Mahalanobis distance?
- The topic indicates learning which correspond to what ?
- What are the main difference and similiarity between prpopsed method and PCA and LDA?
- What can be achieved from the distance?
- Regularization to avoid overfitting
- Maxliklihood --> Minimum distance
- How to minimize (Do we need optimization method ? Or we can solve it without them in much easier way using this method?)
Definition of metric learning
- Definition of Mahalanobis distance
- Specific case of Euclidean distance
- What Mahalahanobis and the covariance matrice will imply
Check the following notebook for an entire description of LMNN. 
 Entropy driven optimisation.
 
