113 citations · 157 across the 8 of their papers we have counts for
8 papers
Multilinear Low-Rank Tensors on Graphs & Applications
Nauman Shahid, Francesco Grassi, Pierre Vandergheynst
We propose a new framework for the analysis of low-rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new gra…
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Faisal Mahmood, Nauman Shahid, Ulf Skoglund +1
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions. Local TV methods fail to…
Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy
Keith Smith, Benjamin Ricaud, Nauman Shahid +6
Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first stud…
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Nauman Shahid, Nathanael Perraudin, Pierre Vandergheynst
Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geo…
Graph Based Sinogram Denoising for Tomographic Reconstructions
Faisal Mahmood, Nauman Shahid, Pierre Vandergheynst +1
Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sino…
Compressive PCA for Low-Rank Matrices on Graphs
Nauman Shahid, Nathanael Perraudin, Gilles Puy +1
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong…
Fast Robust PCA on Graphs
Nauman Shahid, Nathanael Perraudin, Vassilis Kalofolias +2
Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-li…
Robust Principal Component Analysis on Graphs
Nauman Shahid, Vassilis Kalofolias, Xavier Bresson +2
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well…