AAAI Publications, Twenty-Fourth AAAI Conference on Artificial Intelligence

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Transductive Learning on Adaptive Graphs
Yan-Ming Zhang, Yu Zhang, Dit-Yan Yeung, Cheng-Lin Liu, Xinwen Hou

Last modified: 2010-07-03


Graph-based semi-supervised learning methods are based on some smoothness assumption about the data. As a discrete approximation of the data manifold, the graph plays a crucial role in the success of such graph-based methods. In most existing methods, graph construction makes use of a predefined weighting function without utilizing label information even when it is available. In this work, by incorporating label information, we seek to enhance the performance of graph-based semi-supervised learning by learning the graph and label inference simultaneously. In particular, we consider a particular setting of semi-supervised learning called transductive learning. Using the LogDet divergence to define the objective function, we propose an iterative algorithm to solve the optimization problem which has closed-form solution in each step. We perform experiments on both synthetic and real data to demonstrate improvement in the graph and in terms of classification accuracy.


semi-supervised learning; transductive learning; classification;

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