most citedA Statistically Identifiable Model for Tensor-Valued Gaussian Random Variables

6 citations · 7 across the 3 of their papers we have counts for

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eess.SP2019

Graph Theory and Metro Traffic Modelling

Bruno Scalzo Dees, Anthony G. Constantinides, Danilo P. Mandic

In this article we demonstrate how graph theory can be used to identify those stations in the London underground network which have the greatest influence on the functionality of t…

eess.SP20196 cited

A Statistically Identifiable Model for Tensor-Valued Gaussian Random Variables

Bruno Scalzo Dees, Anh-Huy Phan, Danilo P. Mandic

Real-world signals typically span across multiple dimensions, that is, they naturally reside on multi-way data structures referred to as tensors. In contrast to standard ``flat-vie…

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…

eess.SP2019

Unitary Shift Operators on a Graph

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A unitary shift operator (GSO) for signals on a graph is introduced, which exhibits the desired property of energy preservation over both backward and forward graph shifts. For rig…

eess.SP2019

Tight Lower Bound on the Tensor Rank based on the Maximally Square Unfolding

Giuseppe G. Calvi, Bruno Scalzo Dees, Danilo P. Mandic

Tensors decompositions are a class of tools for analysing datasets of high dimensionality and variety in a natural manner, with the Canonical Polyadic Decomposition (CPD) being a m…

eess.SP2019

A Class of Doubly Stochastic Shift Operators for Random Graph Signals and their Boundedness

Bruno Scalzo Dees, Ljubisa Stankovic, Milos Dakovic +2

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper -boundedness for locally stationary random graph signals…