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The insufficiency of labeled training data for representing the distribution of entire dataset is a major obstacle in various practical data mining applications. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, provide possibilities to solve this problem. Graph-based semi-supervised learning has recently become one of the most active research areas. In this paper, a novel graph-based semi-supervised learning approach entitled Class Dissimilarity based Linear Neighborhood Propagation (CD-LNP) is proposed, which assumes that each data point can be linearly reconstructed from its neighborhood. The neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. Our algorithm can propagate the labels from the labeled points to entire data set using these linear neighborhoods with sufficient smoothness. Experiment results demonstrate that our approach outperforms other popular graph-based semi-supervised learning methods. [ABSTRACT FROM AUTHOR]

Similarity learning plays a fundamental role in the problem of database retrieval and nearest classification problem. Traditional pairwise similarity measure ignores the contextual information, and the Graph Transduction (GT) has been proposed as a contextual similarity learning algorithm to utilize the contextual information, which is embedded in a nearest neighbor graph. On main shortage of this method is that it is difficult to choose the optimal graph since different graphs may focus on different aspects of the objects. Co-Transduction (CT) is lately proposed by fusing two different graphs. In this paper, we generalize this problem by using the ensemble of many candidate graphs with different models and parameters for transduction, by assuming that the optimal graph could be obtained by the weighted linear ensemble of these candidate graphs. The similarities and graph weights are modeled within one unified objective function, and optimized alternately in an iterative algorithm. The new proposed algorithm, named as Ensemble Transduction (ET), is tested on two challenging tasks and the experimental results show that it can outperform both the GT and CT. [ABSTRACT FROM AUTHOR]