153 citations · 253 across the 21 of their papers we have counts for
6 papers · 1 filter
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
T. Mitchell Roddenberry, Santiago Segarra, Anastasios Kyrillidis
We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems. The setting we consider involves rank-one sens…
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perh…
Network topology change-point detection from graph signals with prior spectral signatures
Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying g…
Network Topology Inference with Graphon Spectral Penalties
T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra
We consider the problem of inferring the unobserved edges of a graph from data supported on its nodes. In line with existing approaches, we propose a convex program for recovering…
Blind Inference of Eigenvector Centrality Rankings
T. Mitchell Roddenberry, Santiago Segarra
We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of grap…
Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering
T. Mitchell Roddenberry, Santiago Segarra
This paper characterizes the difficulty of estimating a network's eigenvector centrality only from data on the nodes, i.e., with no information about the topology of the network. W…