2 research outputs found
Locality Constraint Dictionary Learning with Support Vector for Pattern Classification
Discriminative dictionary learning (DDL) has recently gained significant
attention due to its impressive performance in various pattern classification
tasks. However, the locality of atoms is not fully explored in conventional DDL
approaches which hampers their classification performance. In this paper, we
propose a locality constraint dictionary learning with support vector
discriminative term (LCDL-SV), in which the locality information is preserved
by employing the graph Laplacian matrix of the learned dictionary. To jointly
learn a classifier during the training phase, a support vector discriminative
term is incorporated into the proposed objective function. Moreover, in the
classification stage, the identity of test data is jointly determined by the
regularized residual and the learned multi-class support vector machine.
Finally, the resulting optimization problem is solved by utilizing the
alternative strategy. Experimental results on benchmark databases demonstrate
the superiority of our proposed method over previous dictionary learning
approaches on both hand-crafted and deep features. The source code of our
proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SVComment: submitted to IEEE Acces
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
The use of sparse representation (SR) and collaborative representation (CR)
for pattern classification has been widely studied in tasks such as face
recognition and object categorization. Despite the success of SR/CR based
classifiers, it is still arguable whether it is the -norm sparsity or
the -norm collaborative property that brings the success of SR/CR
based classification. In this paper, we investigate the use of nonnegative
representation (NR) for pattern classification, which is largely ignored by
previous work. Our analyses reveal that NR can boost the representation power
of homogeneous samples while limiting the representation power of heterogeneous
samples, making the representation sparse and discriminative simultaneously and
thus providing a more effective solution to representation based classification
than SR/CR. Our experiments demonstrate that the proposed NR based classifier
(NRC) outperforms previous representation based classifiers. With deep features
as inputs, it also achieves state-of-the-art performance on various visual
classification tasks