Learning Networked Exponential Families with Network Lasso

Abstract

We propose networked exponential families to jointly leverage the information in the topology as well as the attributes (features) of networked data points. Networked exponential families are a flexible probabilistic model for heterogeneous datasets with intrinsic network structure. These models can be learnt efficiently using network Lasso which implicitly pools or clusters the data points according to the intrinsic network structure and the local likelihood. The resulting method can be formulated as a non-smooth convex optimization problem which we solve using a primal-dual splitting method. This primal-dual method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.

References in corpus (9)

• Cooperative Game Theory Approaches for Network Partitioning
• Learning graphs from data: A signal representation perspective
• Network Lasso: Clustering and Optimization in Large Graphs
• Global Convergence of Online Limited Memory BFGS
• Optimal rates for total variation denoising
• Localized Lasso for High-Dimensional Regression
• Localized Linear Regression in Networked Data
• Provable Algorithms for Inference in Topic Models
• Analysis of Network Lasso for Semi-Supervised Regression