Classifying Big Data over Networks via the Logistic Network Lasso

ABSTRACT We apply network Lasso to solve binary classification (clustering) problems on network structured data. To this end, we generalize ordinary logistic regression to non-Euclidean data defined over a complex network structure. A scalable classification algorithm is obtained by applying the alternating direction methods of multipliers to solve this optimization problem. Index Terms-- compressed sensing, big data over networks, semi-supervised learning, classification, clustering, complex networks, convex optimization I. INTRODUCTION We consider the problem of classifying or clustering a large set of data points which conform to an underlying network structure. Such network-structured datasets arise in a wide range of application domains, e.g., image-and video processing as well as social networks [1].