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 L1 norm to regularize CCRF, which enables CCRF to perform feature selection. We provide a plausible learning method for L1-Regularized CCRF (L1-CCRF) and verify its effectiveness. Moreover, we demonstrate that the proposed L1-CCRF performs well in selecting key features related to the various customers' power usages in Smart Grid.
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