Recovering low-rank and sparse subspaces jointly for enhanced robust representation and
classification is discussed. Technically, we first propose a transductive low-rank and sparse …

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local
structure information among features in an image for the purpose of image-level …

Recently, the sparse coding based codebook learning and local feature encoding have
been widely used for image classification. The sparse coding model actually assumes the …

Most existing low-rank and sparse representation models cannot preserve the local manifold
structures of samples adaptively, or separate the locality preservation from the coding …

Image classification is a fundamental component in modern computer vision systems, where
sparse representation-based classification has drawn a lot of attention due to its robustness …

Recently, sparse representation, which relies on the underlying assumption that samples
can be sparsely represented by their labeled neighbors, has been applied with great …

Sparse coding learns a set of basis functions such that each input signal can be well
approximated by a linear combination of just a few of the bases. It has attracted increasing …

Deriving from the gradient vector of a generative model of local features, Fisher vector
coding (FVC) has been identified as an effective coding method for image classification …

2-D neighborhood preserving projection (2DNPP) uses 2-D images as feature input instead
of 1-D vectors used by neighborhood preserving projection (NPP). 2DNPP requires less …

Feature representation learning, an emerging topic in recent years, has achieved great
progress. Powerful learned features can lead to excellent classification accuracy. In this …