Globally and Locally Consistent Unsupervised Projection
Authors: Hua Wang, Feiping Nie, Heng Huang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been performed on six benchmark data sets, where the promising results validate the proposed method. |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Science Colorado School of Mines, Golden, Colorado 80401, USA Department of Computer Science and Engineering University of Texas at Arlington, Arlington, Texas 76019, USA |
| Pseudocode | Yes | Algorithm 1: The algorithm to solve Eq. (9). ... Algorithm 2: An efficient iterative algorithm to solve the general trace ratio minimization problem in Eq. (8). |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Six benchmark data sets are used in our experiments, including: two UCI data sets: the Dermatology and Ecoli data sets; one object data set: the COIL-20 data set; one digit and character data sets: the Binalpha data set; and two face data sets: the UMIST and AR data sets. |
| Dataset Splits | No | The paper mentions repeating experiments multiple times and reporting the best clustering performance after searching reduced dimensions, but it does not provide specific train/validation/test dataset splits, exact percentages, sample counts, or citations to predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For KLE method and LPP method, we construct nearest-neighbor graph for each data set and set the neighborhood size for graph construction as 10 following (He and Niyogi 2004). ... in practice we empirically set K = 30 for simplicity... We repeat the experiments for 100 times to alleviate the impact of the random initialization of both our iterative algorithm and the K-means clustering method. |