|Supervised Dictionary Learning Using Distance Dependent Indian Buffet Process|
|Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Ali Zarghami, Mahdieh Soleymani Baghshah|
This paper proposes a novel Dictionary Learning (DL) algorithm for pattern classification tasks. Based on the Distance Dependent Indian Buffet Process (DDIBP) model, a shared dictionary for signals belonging to different classes is learned so that the learned sparse codes are highly discriminative which can improve the pattern classification performance. Moreover, using this non-parametric method, an appropriate dictionary size can be infered. The proposed method evaluated on different standard databases demonstrates higher classification accuracy than other existing DL based classification methods.