Approximating Orthogonal Matrices with Effective Givens Factorization
Authors: Thomas Frerix, Joan Bruna
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate numerical results of approximating the graph Fourier transform... |
| Researcher Affiliation | Academia | 1Technical University of Munich 2New York University. |
| Pseudocode | Yes | Algorithm 1 Coordinate descent on the L1-criterion |
| Open Source Code | Yes | An implementation of these algorithms can be found at https://github.com/tfrerix/ givens-factorization |
| Open Datasets | Yes | MINNESOTA 2642 3304 (Defferrard et al.) HUMANPROTEIN 3133 6726 (Rual et al., 2005) EMAIL 1133 5451 (Guimer a et al., 2003) FACEBOOK 2888 2981 (Mc Auley & Leskovec, 2012) |
| Dataset Splits | No | The paper describes the generation of datasets (planted models, Barabasi-Albert graphs, and uses real-world graph datasets) but does not specify any train/validation/test splits, percentages, or explicit methodologies for partitioning data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | While an implementation link is provided, the paper does not explicitly list software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) within its text. |
| Experiment Setup | Yes | To obtain a Givens sequence, we factorize these samples with manifold coordinate descent on the L1-objective (13). Along the optimization path, we define Nϵ(U) as the number of Givens factors for which the normalized approximation error (3) is smaller than ϵ = 0.1, i.e., Nϵ(U) := min N ||U − G1 . . . GN ||F,sym < ϵ |