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Found 7 papers, 0 papers with code
Multilinear Low-Rank Tensors on Graphs & Applications
We propose a new framework for the analysis of low-rank tensors which lies at the intersection of spectral graph theory and signal processing.
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions.
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space.
Graph Based Sinogram Denoising for Tomographic Reconstructions
This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure.
Compressive PCA for Low-Rank Matrices on Graphs
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements.
Fast Robust PCA on Graphs
Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models.
Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering.
