The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
Authors: Feiping Nie, Xiaoqian Wang, Michael Jordan, Heng Huang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new graph-based clustering method. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University of Texas, Arlington 2Departments of EECS and Statistics, University of California, Berkeley |
| Pseudocode | Yes | Algorithm 1 Algorithm to solve JCLR L2 in Eq. (1). Algorithm 2 Algorithm to solve JCLR L1 in Eq. (2). |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code for the described methodology. |
| Open Datasets | Yes | Yeast (Asuncion and Newman 2007), Abalone (Asuncion and Newman 2007), COIL20 (Nene, Nayar, and Murase 1996b), COIL100 (Nene, Nayar, and Murase 1996a), AR (Martinez 1998), XM2VTS (XM2VTS ) and UMIST (Graham and Allinson 1998) |
| Dataset Splits | No | The paper discusses synthetic and benchmark datasets and mentions using ground truth for clustering, but does not specify explicit train/validation/test splits or refer to a validation set. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For both self-tune Gaussian and our method, we set the number of neighbors, m, to be five for the affinity matrix construction. As for our clustering method, we determined the value of λ in a heuristic way to accelerate the procedure: first set λ with a small value, then in each iteration, we computed the number of zero eigenvalues in LS, if it was larger than k, we divided λ by two; if smaller we multiplied λ by two; otherwise we stopped the iteration. For all the methods involving K-means, including K-means, RCut and NCut methods, we used the same initialization and repeated 50 times to compute their respective best initialization vector in terms of objective value of K-means. |