Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
Authors: Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both algorithms are evaluated on synthetic and real data. They are shown to perform as good or better than their competitors in terms of both numerical performance and scalability. [...] The proposed algorithms are tested and compared to state-of-the-art approaches using several synthetic and real datasets (Section 7). The experimental results show that our approach achieves similar or better performance than existing methods, while reducing significantly the need of computing resources. |
| Researcher Affiliation | Academia | Pierre Humbert1 EMAIL Batiste Le Bars1 EMAIL Laurent Oudre EMAIL Argyris Kalogeratos EMAIL Nicolas Vayatis EMAIL Centre Borelli, ENS Paris-Saclay, Universit e Paris-Saclay, CNRS, F-94235 Cachan, France. |
| Pseudocode | Yes | Algorithm 1 The IGL-3SR algorithm with ℓ2,1-norm [...] Algorithm 2 The FGL-3SR algorithm with ℓ2,1-norm |
| Open Source Code | Yes | The source code of our implementations is available at https://github.com/pierreHmbt/GL-3SR. |
| Open Datasets | Yes | We used hourly temperature (C ) measurements on 32 weather stations in Brittany, France, during a period of 31 days (Chepuri et al., 2017). [...] RNA-Seq Cancer Genome Atlas Research Network data (Weinstein et al., 2013). [...] we consider the Attention Deficit Hyperactivity Disorder (ADHD) dataset (Bellec et al., 2017) composed of functional Magnetic Resonance Imaging (f MRI) data. |
| Dataset Splits | Yes | We study the resting-state f MRI of 20 subjects with ADHD and 20 healthy subjects available from Nilearn (Abraham et al., 2014). [...] we use a 3-Nearest Neighbors classification algorithm. We use the correlation coefficient of Equation (24) as distance metric between Laplacian matrices, and a leave-one-out cross-validation strategy. |
| Hardware Specification | Yes | All experiments were conducted on a single personal computer: a personal laptop with with 4-core 2.5GHz Intel CPUs and Linux/Ubuntu OS. |
| Software Dependencies | No | For the Λ-step of both algorithms, we use the Python s CVXPY package (Diamond and Boyd, 2016). For the X-step of IGL-3SR, we use the conjugate gradient descent solver combined with an adaptive line search, both provided by Pymanopt (Townsend et al., 2016), a Python toolbox for optimization on manifolds. Graphs are generated with Network X (Hagberg et al., 2008), Networ Kit (Staudt et al., 2016), and SNAP (Leskovec and Sosiˇc, 2016). |
| Experiment Setup | Yes | For all the methods, the hyperparameters α and β are set by maximizing the F1-measure on the thresholded c W, as explained in Section 7.1. [...] we set α = 10 4, and β is chosen so that we obtain a 2-sparse spectral representation, which this last assumes that there are two clusters of weather stations. |