Tensor Decomposition via Simultaneous Power Iteration
Authors: Po-An Wang, Chi-Jen Lu
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show how to find the eigenvectors simultaneously with the help of a new initialization procedure. This allows us to achieve a better running time in the batch setting, as well as a lower sample complexity in the streaming setting. Our algorithm is given in Algorithm 1, which consists of two phases: the initialization phase and the tensor power phase. Due to the space limitation, we will move all our proofs to the appendix in the supplementary material. |
| Researcher Affiliation | Academia | Po-An Wang 1 Chi-Jen Lu 1 1Academia Sinica, Taiwan. |
| Pseudocode | Yes | Algorithm 1 Robust tensor power method |
| Open Source Code | No | The paper is theoretical and does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper discusses theoretical models of data (e.g., 'noisy version of the tensor', 'stream of vectors') but does not specify a publicly available dataset used for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, such as hyperparameters or training configurations. |