Wang et al. [22] adopt squared templates to replace trivial templates to handle partial occlusion and propose a probabilistic collaborative representation framework, which reduces the complexity in traditional sparse model-based methods.
Motivated by these concerns, in this paper, we propose a novel kernel collaborative representation based classifier (KCRC), and then we use it as a criterion to design a kernel collaborative representation based dictionary learning and discriminant projection method (KDL-DP).
In addition, to enhance the efficiency of classification procedure, we design a low-rank structured collaborative representation classification scheme for MLSDL.
In this paper, we propose a new metric that is dissimilarity metric based on collaborative representation (CR) method, which represents the relation between a test sample and a class from a new viewpoint and provides the information different from that offered by the distance metric in conventional sparse representation (SR) methods.
Multi-modalities action recognition becomes a hot research topic, and this paper proposes a collaborative sparse representation leaning model for RGB-D action recognition where RGB and depth information are adaptive fused.
This paper presents an effective scene classification approach based on collaborative representation fusion of local and global spatial features.
Course will feature guest appearances by representatives from a range of social movements at Stanford the past fifty years, and the building of an online repository and community for the collaborative representation and discussion of history.
Class will apply lessons from digital history practice and theory to the design of an online repository and community for the collaborative representation and discussion of social movement history at Stanford, and to the further development of source material in a future version of the class.
To improve the performance of CRC, this paper proposes a new dimensionality reduction method called Optimized Projection for Collaborative Representation based Classification (OP-CRC), which has the direct connection to CRC.
The ScSPM method is based on sparse representation, but l 2 regularizer can also be used with a sufficiently over-complete dictionary according to the studies of collaborative representation [32, 33].
This paper proposes a new SR method with multi-channel constraints, which integrates clustering, collaborative representation, and progressive multi-layer mapping relationships to reconstruct high-resolution (HR) color image.
