Abstract
Continuous Conditional Random Fields (CCRF) has been widely applied to various research domains as an efficient approach for structural regression. In previous studies, the weights of CCRF are constrained to be positive from a theoretical perspective. This paper extends the definition domains of weights of CCRF and thus introduces \(L_1\) norm to regularize CCRF, which enables CCRF to perform feature selection. We provide a plausible learning method for \(L_1\)-Regularized CCRF (\(L_1\)-CCRF) and verify its effectiveness. Moreover, we demonstrate that the proposed \(L_1\)-CCRF performs well in selecting key features related to the various customers’ power usages in Smart Grid.
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Wang, X., Ren, F., Liu, C., Zhang, M. (2016). \(L_1\)-Regularized Continuous Conditional Random Fields. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_67
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DOI: https://doi.org/10.1007/978-3-319-42911-3_67
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