Tensor Decomposition via Joint Matrix Schur Decomposition
Authors: Nicolo Colombo, Nikos Vlassis
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that our algorithm is faster and at least as accurate and robust than state-of-the-art algorithms for this problem. 4. Experiments 4.1. Comparison on synthetic data Figure 1. Decomposition of a symmetric nonorthogonal tensor. |
| Researcher Affiliation | Collaboration | Nicol o Colombo NICOLO.COLOMBO@UNI.LU Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur Alzette, Luxembourg Nikos Vlassis VLASSIS@ADOBE.COM Adobe Research, San Jose, CA |
| Pseudocode | No | The paper describes the Gauss-Newton algorithm mathematically and verbally in Section 3, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the proposed methodology is publicly available. |
| Open Datasets | Yes | To test the performance of our algorithm on real-world data we have chosen a label prediction problem from crowdsourcing data. The problem and the dataset are described by Zhang et al. (2014) where an estimator based on orderthree moments is also proposed. |
| Dataset Splits | No | The paper discusses generating synthetic data and using a real-world dataset from a cited work, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, counts, or predefined split references). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab codes' for comparison methods but does not provide specific version numbers for Matlab or any other software dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the algorithmic steps and initialization strategy for the Gauss-Newton algorithm, but it does not provide specific numerical values for hyperparameters (e.g., learning rate, batch size, number of iterations/epochs) or detailed convergence criteria for the experimental setup. |