Non-Negative Matrix Factorization with Sinkhorn Distance
Authors: Wei Qian, Bin Hong, Deng Cai, Xiaofei He, Xuelong Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows the encouraging results of the proposed algorithm comparing with other NMF methods. In this section, we test the performance of the proposed algorithm in the context of two challenging tasks. |
| Researcher Affiliation | Academia | State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, China {qwqjzju, hongbinzju, dengcai}@gmail.com xiaofeihe@cad.zju.edu.cn Xi an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China xuelong li@opt.ac.cn |
| Pseudocode | No | The paper describes mathematical update rules and references an algorithm from another paper, but does not provide pseudocode or a clearly labeled algorithm block for its own method. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about releasing the source code for their method. |
| Open Datasets | Yes | The data set used in this section is COIL20 image library [Nene et al., 1996] |
| Dataset Splits | No | The paper performs clustering experiments and reports results on the COIL20 dataset, but does not specify explicit training, validation, and test splits in percentages or counts. It mentions "10 test runs were conducted on different randomly chosen clusters" for evaluation. |
| Hardware Specification | Yes | Our algorithm needs only 2.9 seconds per full iteration and converges after 290.9 seconds by using Matlab on an Intel i7-4790K 4.0GHz processor on a data set with 1440 samples and 1024 features. |
| Software Dependencies | No | The paper mentions using "Matlab" for computations, but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Following [Cai et al., 2011], we use binary weighting scheme for constructing the 5-nearest neighbor graph and set the regularization parameter λ to 100. We set the λ and γ to 100 and 10 respectively and use the 2D distance of pixels location of the image as the ground metric. We set the λ , γ and to 100,1 and 10 respectively and use the 2D distance of pixels location of the image as the ground metric. |