Classifying Partially Labeled Networked Data via Logistic Network Lasso
This work addresses classification in networked data with limited labels, but it is incremental as it builds on existing network Lasso methods.
The paper tackles the problem of classifying partially labeled networked data by applying logistic network Lasso to leverage network structure for statistical strength, resulting in a scalable message passing algorithm for big data applications.
We apply the network Lasso to classify partially labeled data points which are characterized by high-dimensional feature vectors. In order to learn an accurate classifier from limited amounts of labeled data, we borrow statistical strength, via an intrinsic network structure, across the dataset. The resulting logistic network Lasso amounts to a regularized empirical risk minimization problem using the total variation of a classifier as a regularizer. This minimization problem is a non-smooth convex optimization problem which we solve using a primal-dual splitting method. This method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.