Tensor Biclustering
Authors: Soheil Feizi, Hamid Javadi, David Tse
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show the efficiency of our proposed method in several synthetic and real datasets. |
| Researcher Affiliation | Academia | Soheil Feizi Stanford University sfeizi@stanford.edu Hamid Javadi Stanford University hrhakim@stanford.edu David Tse Stanford University dntse@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Tensor Folding+Spectral Input: T , k Compute ˆu1, the top eigenvector of C1 Compute ˆw1, the top eigenvector of C2 Compute ˆJ1, indices of the k largest values of | ˆw1| Compute ˆJ2, indices of the k largest values of |ˆu1| Output: ˆJ1 and ˆJ2 |
| Open Source Code | Yes | We provide code for tensor biclustering methods in the following link: https://github.com/ Soheil Feizi/Tensor-Biclustering. |
| Open Datasets | Yes | In this section we apply tensor biclustering methods to the roadmap epigenomics dataset [4] which provides histon mark signal strengths in different segments of human genome in various tissues and cell types. [4] Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi Moussavi, Pouya Kheradpour, Zhizhuo Zhang, Jianrong Wang, Michael J Ziller, et al. Integrative analysis of 111 reference human epigenomes. Nature, 518(7539):317 330, 2015. |
| Dataset Splits | No | The paper mentions using synthetic and real datasets, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits with citations). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as CPU or GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In our simulations we consider n = 200, m = 50, k = 40. (Section 6.1) We use the tensor folding+spectral algorithm 1 with |J1| = 10 and |J2| = 400 (Section 6.2) |