Leveraging Union of Subspace Structure to Improve Constrained Clustering
Authors: John Lipor, Laura Balzano
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of our method and the nonparametric version of the URASC algorithm (URASC-N) over a variety of datasets. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University Michigan, Ann Arbor, MI, USA. Correspondence to: John Lipor <lipor@umich.edu>. |
| Pseudocode | Yes | Pseudocode for the complete algorithm is given in Algorithm 1. |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We consider five datasets commonly used as benchmarks in the subspace clustering literature3, with a summary of the datasets and their relevant parameters are given in Table 1. The Yale B dataset... The MNIST handwritten digit database... The COIL-20 dataset (Nene et al., 1996b)... The COIL-100 dataset (Nene et al., 1996a)... Finally, we apply our algorithm to the USPS dataset provided by (Cai et al., 2011)... |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts) needed for reproduction. It mentions using '100 randomly selected subsets of size K' for some datasets but not standard splits. |
| Hardware Specification | No | The paper mentions 'using 10 cores' for runtime measurements but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We use a maximum query budget of 2K for UOS-EXPLORE and EXPLORE. For the Yale dataset... We choose d = 9 as is common in the literature... for MNIST dataset... using subspace dimension d = 3. |