Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
Authors: Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on both synthetic data and image clustering to support our result. |
| Researcher Affiliation | Academia | Zhihui Zhu Mathematical Institute for Data Science Johns Hopkins University Baltimore, MD, USA EMAIL Xiao Li Department of Electronic Engineering The Chinese University of Hong Kong Shatin, NT, Hong Kong EMAIL Kai Liu Department of Computer Science Colorado School of Mines Golden, CO, USA EMAIL Qiuwei Li Department of Electrical Engineering Colorado School of Mines Golden, CO, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Sym ANLS Initialization: k = 1 and U 0 = V 0. 1: while stop criterion not meet do 2: U k = arg min V 0 1 2 X UV T k 1 2 F + λ 2 U V k 1 2 F ; 3: V k = arg min U 0 1 2 X U k V T 2 F + λ 2 U k V 2 F ; 4: k = k + 1. 5: end while Output: factorization (U k, V k). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or explicit code release statement by the authors. |
| Open Datasets | Yes | We also test on real world dataset CBCL 5, where there are 2429 face image data with dimension 19 19. Footnote 5: http://cbcl.mit.edu/software-datasets/Face Data2.html ORL: 400 facial images from 40 different persons with each one has 10 images from different angles and emotions 6. Footnote 6: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html COIL-20: 1440 images from 20 objects 7. Footnote 7: http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php TDT2: 10,212 news articles from 30 categories 8. Footnote 8: https://www.ldc.upenn.edu/collaborations/past-projects MNIST: classical handwritten digits dataset 9, where 60,000 are for training (denoted as MNISTtrain), and 10,000 for testing (denoted as MNISTtest). Footnote 9: http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | Yes | MNIST: classical handwritten digits dataset 9, where 60,000 are for training (denoted as MNISTtrain), and 10,000 for testing (denoted as MNISTtest). |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. It lacks details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions initializing algorithms with uniformly distributed entries and tuning the lambda parameter ('we tune the best parameter λ for each experiment'). However, it does not provide specific hyperparameter values, training configurations, or system-level settings typically found in an experimental setup description (e.g., learning rates, batch sizes, optimizers). |