DML
DML Sharif University of Technology
Locality preserving discriminative dictionary learning
  Nov   2014       Classification Supervised Learning Discriminative Dictionary Learning Locality Preserving
S. Haghiri , H.R. Rabiee , A. Soltani-Farani , S.A. Hosseini and M. Shadloo
In this paper, a novel discriminative dictionary learning approach is proposed that attempts to preserve the local structure of the data while encouraging discriminability. The reconstruction error and sparsity inducing ℓ 1 -penalty of dictionary learning are minimized alongside a locality preserving and discriminative term. In this setting, each data point is represented by a sparse linear combination of dictionary atoms with the goal that its k-nearest same-label neighbors are preserved. Since the class of a new data point is unknown, its sparse representation is found once for each class. The class that produces the lowest error is associated with that point. Experimental results on five common classification datasets, show that this method outperforms state-of-the-art classifiers, especially when the training data is limited.
Type
Conference
Publisher
IEEE
ISSN
2381-8549
ISBN
978-1-4799-5751-4
Accession
14884101
Location
Paris, France