Abstract
Label propagation (LP) is used in the framework of semi-supervised learning. In this paper, we propose a novel method of logistic label propagation (LLP). The proposed method employs logistic functions for accurately estimating the label values as the posterior probabilities. In LLP, the label of newly input sample is efficiently estimated by using the optimized coefficients in the logistic function, without such recomputation of all label values as in original LP. In the experiments on classification, the proposed method produced more reliable label values at the high degree of confidence than LP and ordinary logistic regression. In addition, even for a small portion of the labeled samples, the error rates by LLP were lower than those by the logistic regression.
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Watanabe, K., Kobayashi, T., Otsu, N. (2010). Logistic Label Propagation for Semi-supervised Learning. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_57
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DOI: https://doi.org/10.1007/978-3-642-17537-4_57
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