1 research outputs found
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?
Spare representation of signals has received significant attention in recent
years. Based on these developments, a sparse representation-based
classification (SRC) has been proposed for a variety of classification and
related tasks, including face recognition. Recently, a class dependent variant
of SRC was proposed to overcome the limitations of SRC for remote sensing image
classification. Traditionally, greedy pursuit based method such as orthogonal
matching pursuit (OMP) are used for sparse coefficient recovery due to their
simplicity as well as low time-complexity. However, orthogonal least square
(OLS) has not yet been widely used in classifiers that exploit the sparse
representation properties of data. Since OLS produces lower signal
reconstruction error than OMP under similar conditions, we hypothesize that
more accurate signal estimation will further improve the classification
performance of classifiers that exploiting the sparsity of data. In this paper,
we present a classification method based on OLS, which implements OLS in a
classwise manner to perform the classification. We also develop and present its
kernelized variant to handle nonlinearly separable data. Based on two
real-world benchmarking hyperspectral datasets, we demonstrate that class
dependent OLS based methods outperform several baseline methods including
traditional SRC and the support vector machine classifier