The selection of initial values of hyperparameters is extremely important for their robust optimization. σ 1 , σ 2 , the bandwidths associated with the Gaussian bases are prespecified as, σ 1 2 = 1 (Kx×p) Kx i Kx j (µ i -µ j ) 2 , and σ 2 2 = 1 (Ky×q) Ky i Ky j (γ i -γ j ) 2 Here, K x and K z represent the number of Gaussian basis functions for each feature, and µ k and γ k are the centers associated with the Gaussian bases corresponding to the chemical and graphical descriptors, (x i ) ,(z i ) respectively. To initialize µ k , γ k , we follow a two-step mechanism. First, we form 10 disjoint datasets with 600 datapoints each that are randomly selected from the original data. We first run the relevance vector machine (RVM) and get the number of relevance vectors identified by RVM. Specifically, the RVM model with Gaussian kernel shares some commonalities with the RBF-net and it was found to work well in . For all of those data partitions, the numbers turned out to be around 30. We then fit the proposed model with 30 bases and random initializations applying the subgradient descent for all the unknown parameters. The optimized sets of bases are stored, leading to 300 choices for µ's and γ's. This step was run only once for all the subsequent model fittings. Finally, we initialize µ k and γ k by choosing K x and K z -many centers by applying the k-means clustering on those 300 candidate bases. Here, K x and K z are set based on the RVM output when applied to the full data.