Kernel learning apparatus using transformed convex optimization problem

A system executed by multiple computer nodes that are capable of communicating with each other, comprising:

• a first computer node, which includes:
• a data preprocessing circuit configured to preprocess and to represent each data example as a collection of feature representations that to need to be interpreted:
• a feature mapping circuit configured to design a kernel function with an explicit feature map to embed the feature representations of the data into a nonlinear feature space and to produce the explicit feature map of the designed kernel function to train a machine learning model;
• a convex problem formulating circuit configured to formulate the non-convex problem for training the machine learning model into a convex optimization problem based on the explicit feature map;
• and
• an alternating direction method and multipliers (ADMM) transforming circuit configured to transform the convex optimization problem into an ADMM form where sub-problems can be solved separately and efficiently,
• a plurality of second computer nodes, each including:
• a model training circuit configured to perform ADMM iterations until convergence in a distributed fashion to train an interpretable machine learning model.