activity
20172021
most citedGraph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs

11 citations · 30 across the 8 of their papers we have counts for

collaborators

16 papers

q-fin.ST2021

Nonstationary Portfolios: Diversification in the Spectral Domain

Bruno Scalzo, Alvaro Arroyo, Ljubisa Stankovic +1

Classical portfolio optimization methods typically determine an optimal capital allocation through the implicit, yet critical, assumption of statistical time-invariance. Such model…

eess.SP2020

A Probabilistic Spectral Analysis of Multivariate Real-Valued Nonstationary Signals

Bruno Scalzo, Ljubisa Stankovic, Danilo P. Mandic

A class of multivariate spectral representations for real-valued nonstationary random variables is introduced, which is characterised by a general complex Gaussian distribution. In…

cs.IT20206 cited

The Support Uncertainty Principle and the Graph Rihaczek Distribution: Revisited and Improved

Ljubisa Stankovic

The classical support uncertainty principle states that the signal and its discrete Fourier transform (DFT) cannot be localized simultaneously in an arbitrary small area in the tim…

cs.IT2020

RANSAC-Based Signal Denoising Using Compressive Sensing

Ljubisa Stankovic, Milos Brajovic, Isidora Stankovic +2

In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven s…

cs.IT20206 cited

Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic +4

Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition,…

eess.SP2019

Portfolio Cuts: A Graph-Theoretic Framework to Diversification

Bruno Scalzo Dees, Ljubisa Stankovic, Anthony G. Constantinides +1

Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate…