Fast Kernel Sparse Representation Classifier using Improved Smoothed-l 0 Norm.
作者： Dzati Athiar Ramli , Tan Wan Chien
DOI: 10.1016/J.PROCS.2017.08.148
关键词: Basis pursuit 、 Norm (mathematics) 、 Kernel method 、 Smoothing 、 Pattern recognition 、 Quadratic classifier 、 Classifier (UML) 、 Computer science 、 Kernel (linear algebra) 、 Sparse approximation 、 Solver 、 Artificial intelligence
摘要: Abstract The computation time for solving classification problem using sparse representation classifier remains a huge drawback as it is to be implemented in real applications. consuming of mainly due the signal recovery solver which based on l 1 minimization or Basis Pursuit. Since then, fast version introduced and smoothing discontinuous properties 0 norm. In this work, smoothed norm algorithm. This also modified improved such way increase its accuracy further reduce time. use kernel described paper. Experiments human speech data are carried out order compare with state art classifiers. Experimental results prove that proposed algorithm greatly reduced compared baseline performances.
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