Supervised Dictionary Learning Using Distance Dependent Indian Buffet Process
Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Ali Zarghami, Mahdieh Soleymani Baghshah

Abstract:
‎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.‎‎‎