A posteriori error bounds for joint matrix decomposition problems
Authors: Nicolo Colombo, Nikos Vlassis
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the bound on synthetic data for which the ground truth is known. |
| Researcher Affiliation | Collaboration | Nicolò Colombo Department of Statistical Science University College London nicolo.colombo@ucl.ac.uk Nikos Vlassis Adobe Research San Jose, CA vlassis@adobe.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper states 'We created a set of synthetic problems in which the ground truth is known'. This indicates generated data, not a publicly available dataset with access information. |
| Dataset Splits | No | The paper evaluates an error bound using synthetic data and different algorithms, but it does not specify traditional train/validation/test dataset splits for model training or evaluation, as the experiment is not about training a machine learning model. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Gauss-Newton algorithm [8]' and 'Jacobi algorithm [13] (our implementation)' but does not provide specific software versions for these or any other dependencies. |
| Experiment Setup | Yes | For each set Mσ = { M̂n}N n=1, two approximate joint triangularizers were computed by optimizing (4) using two different iterative algorithms, the Gauss-Newton algorithm [8], and the Jacobi algorithm [13] (our implementation), initialized with the same random orthogonal matrix. ... We considered two settings, N = 5 and N = 100, and several different noise levels obtained by varying the perturbation parameter σ. |