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In this paper, we consider the problem of semi-supervised dimensionality reduction. We focus on the local geometric structure of data and propose a novel method, called Semi-supervised Locality Discriminant Projections (SSLDP). It uses both labeled and unlabeled samples. Specifically, the labeled samples are used to explore the discriminating ainformation including both similarity and dissimilarity...
In this paper, we propose a novel algorithm that extends the classical probabilistic models to semi-supervised learning framework via manifold regularization. This regularization is used to control the complexity of the model as measured by the geometry of the distribution. Specifically, the intrinsic geometric structure of data is modeled by an adjacency graph, then, the graph Laplacian, analogous...
Recently, a semi-supervised learning algorithm called ASO (Alternating Structure Optimization) has been proposed, which belongs to linear structural learning. It utilizes a number of auxiliary problems (APs) with unlabelled data and then extracts common structural parameter of APs to improve the performances of the target problems (TPs). How to select the appropriate APs is the keystone of ASO algorithm...
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