Abstract
A novel semi-supervised classification framework is proposed based on the label propagation using random walks on graph. To characterize this model, two classifiers, namely the lazy and single-step random walk classifiers are specifically derived. Sufficient experiments and comparison prove their universal adaptability and good performance.
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Xu, X., Lu, L., He, P., Pan, Z., Chen, L. (2012). Random Walk Classifier Framework on Graph. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_6
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DOI: https://doi.org/10.1007/978-3-642-33506-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
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