2 research outputs found
Supervised classification via constrained subspace and tensor sparse representation
SRC, a supervised classifier via sparse representation,
has rapidly gained popularity in recent years and can be
adapted to a wide range of applications based on the sparse
solution of a linear system. First, we offer an intuitive geometric
model called constrained subspace to explain the mechanism
of SRC. The constrained subspace model connects the dots
of NN, NFL, NS, NM. Then, inspired from the constrained
subspace model, we extend SRC to its tensor-based variant,
which takes as input samples of high-order tensors which are
elements of an algebraic ring. A tensor sparse representation is
used for query tensors. We verify in our experiments on several
publicly available databases that the tensor-based SRC called
tSRC outperforms traditional SRC in classification accuracy.
Although demonstrated for image recognition, tSRC is easily
adapted to other applications involving underdetermined linear
systems