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Found 7 papers, 3 papers with code
Large Scale Graph Learning from Smooth Signals
In this paper, we show how to scale it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$, with quality that approaches the exact graph learning model.
How to learn a graph from smooth signals
We propose a framework that learns the graph structure underlying a set of smooth signals.
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs.
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.
Matrix Completion on Graphs
Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.
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UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions.
