Efficient structure learning with automatic sparsity selection for causal graph processes

We propose a novel algorithm for efficiently computing a sparse directed adjacency matrix from a group of time series following a causal graph process. Our solution is scalable for both dense and sparse graphs and automatically selects the LASSO coefficient to obtain an appropriate number of edges in the adjacency matrix. Current state-of-the-art approaches rely on sparse-matrix-computation libraries to scale, and either avoid automatic selection of the LASSO penalty coefficient or rely on the prediction mean squared error, which is not directly related to the correct number of edges. Instead, we propose a cyclical coordinate descent algorithm that employs two new non-parametric error metrics to automatically select the LASSO coefficient. We demonstrate state-of-the-art performance of our algorithm on simulated stochastic block models and a real dataset of stocks from the S&P500.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/18/2013

A Component Lasso

We propose a new sparse regression method called the component lasso, ba...
research
04/30/2022

A Faster Algorithm for Betweenness Centrality Based on Adjacency Matrices

Betweenness centrality is essential in complex network analysis; it char...
research
11/28/2009

Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs

Directed acyclic graphs (DAGs) are commonly used to represent causal rel...
research
06/18/2012

Group Sparse Additive Models

We consider the problem of sparse variable selection in nonparametric ad...
research
12/03/2019

Decentralised Sparse Multi-Task Regression

We consider a sparse multi-task regression framework for fitting a colle...
research
09/29/2015

Estimating network edge probabilities by neighborhood smoothing

The estimation of probabilities of network edges from the observed adjac...
research
01/03/2012

Learning joint intensity-depth sparse representations

This paper presents a method for learning overcomplete dictionaries comp...

Please sign up or login with your details

Forgot password? Click here to reset