Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
Authors: Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 zzhu29@jhu.edu Xiao Li Department of Electronic Engineering The Chinese University of Hong Kong Shatin, NT, Hong Kong xli@ee.cuhk.edu.hk Kai Liu Department of Computer Science Colorado School of Mines Golden, CO, USA kaliu@mines.edu Qiuwei Li Department of Electrical Engineering Colorado School of Mines Golden, CO, USA qiuli@mines.edu |
| 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). |