Partial Multi-View Clustering
Authors: Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two-view data demonstrate the advantages of our proposed approach. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 The PVC Approach |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | The Web KB data set1 (Blum and Mitchell 1998) has been widely used in multi-view learning (Guo 2013; Zhang and Huan 2012), which contains webpages collected from four universities: Cornell, Texas, Washington and Wisconsin. 1http://membres-liglab.imag.fr/grimal/data.html |
| Dataset Splits | Yes | Each time we randomly select 10% to 90% examples, with 20% as interval, as partial examples. Such process is repeated 10 times and the average and standard deviation results are recorded. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For PVC, the latent dimension t is set as the number of clusters, the default choice for most subpace approaches. The sparsity tradeoff parameter λ is fixed as 0.01 for all data sets. |