Semi-supervised Penalized Output Kernel Regression for Link Prediction

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Learning Theory for Distribution Regression (2014, background): “, 2016), or structured output prediction (Brouard et al., 2011; Kadri et al., 2013) fall under the same umbrella: they can be naturally phrased as learning vectorvalued functions (Micchelli and Pontil, 2005).”
Link prediction in social networks: the state-of-the-art (2014, methodology): “Brouard et al. also addressed link prediction as an output kernel learning task through semi-supervised output kernel regression [68].”
Operator-valued Kernels for Learning from Functional Response Data (2015, methodology): “…kernel methods are in multi-task learning (Evgeniou et al., 2005; Micchelli and Pontil, 2005b), even though some successful applications also exist in other areas, such as image colorization (Minh et al., 2010), link prediction (Brouard et al., 2011) and network inference (Lim et al., 2015).”
Input Output Kernel Regression (2016, methodology): “Considering the least-squares cost, the optimization problem becomes: Theorem 6 (Brouard et al. (2011); Minh and Sindhwani (2011)) The coe � cients c 2 F , j = 1 , · · · , ` + n of the expansion admitted by the minimizer ˆ h of the optimization problem (7) satisfy this equation: where J 2 B ( F )…”