Pseudo Supervised Matrix Factorization in Discriminative Subspace
Authors: Jiaqi Ma, Yipeng Zhang, Lefei Zhang, Bo Du, Dapeng Tao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple benchmark datasets illustrate that the proposed model outperforms other state-of-the-art clustering algorithms. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University 2School of Information Science and Engineering, Yunnan University |
| Pseudocode | Yes | Algorithm 1 Algorithm to solve problem (17) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | There are in total nine datasets used in experiments, including one object image dataset, i.e. COIL20 [Cai et al., 2011b], two face image datasets, i.e. YALE [He et al., 2005] and UMIST [Wechsler et al., 2012], and six datasets from the UCI Machine Learning Repository, i.e. Dermatology, Movement, Scale, Iris, Automobile, and Lung-discrete. |
| Dataset Splits | No | The paper lists the datasets used but does not specify any training, validation, or test splits. For clustering tasks, it's common to evaluate on the entire dataset, but explicit split details are not provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only mentions using a 'Matlab method' for K-means. |
| Software Dependencies | No | The paper mentions using a 'Matlab method' for K-means but does not specify version numbers for Matlab or any other software libraries or dependencies used for the experiments. |
| Experiment Setup | Yes | For GNMF and RMNMF, we set the regularization parameters α and µ by searching the grid of {10 5, 10 4, ..., 104, 105}. For RMMF, we set the number of iterations as 200 and choose the regularization parameters α and β by searching the grid of {10 5, 10 4, ..., 104, 105}. Finally, for the proposed PSMF method, we set the number of iterations as 50 and choose the regularization parameters λ1 and λ2 by searching the grid of {10 5, 10 4, ..., 104, 105}. |