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Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and...
Semi-supervised learning works on utilizing both labeled and unlabeled data to improve learning performance, which has been receiving increasing attention in many applications such as clustering and classification. In this paper, we focus on the semi-supervised learning methods developed on data graph whose edge weights are measured by low-rank representation (LRR) coefficients. Specifically, we impose...
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