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The criterion of OP-CRC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously.
The cooperative appearance model uses local collaborative representation to rectify holistic representation with impact regions.
CRC uses the minimum reconstruction residual based on collaborative representation as the decision rule.
It outperforms original sparse representation methods, collaborative representation and linear regression classification.
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).
Recently research has shown that the collaborative representation mechanism is more important to the success of SRC.
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Finally, kernel collaborative representation-based classification (KCRC) is employed on the extracted local and global features, and class label of the testing image is assigned according to the minimal approximation residual after fusion.
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.
But the project quickly grew into a collaborative study of representation.
The UKTLC is a collaborative group (with representation from SHOT, NBTC, the Institute of Biomedical Science (IBMS), and UK NEQAS (blood transfusion and laboratory practice)) whose purpose was to produce recommendations for minimum standards for hospital transfusion laboratories (Chaffe et al, 2009, 2010).
This multi-path video approach was designed to circumvent limitations of traditionally linear video for use as a shared representation in collaborative knowledge building activities.
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