SLR-MVTC: Smooth Low-Rank Multi-View Tensor Clustering
Authors: Zhen Long, Yipeng Liu, Yazhou Ren, Ce Zhu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six multi-view datasets demonstrate that SLR-MVTC outperforms state-of-the-art algorithms in terms of clustering performance and CPU time. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China in Chengdu, 611731, China EMAIL |
| Pseudocode | Yes | Algorithm 1: t-SVD Algorithm 2: Updating Y Algorithm 3: SLR-MVTC |
| Open Source Code | Yes | Code https://github.com/longzhen520/SLR MVTC |
| Open Datasets | Yes | Six well-known multi-view datasets were selected to evaluate the performance of SLR-MVTC: ORL (Samaria and Harter 1994), CCV (Jiang et al. 2011), ALOI 100 (Geusebroek, Burghouts, and Smeulders 2005), Reuters (Lewis et al. 2004), Aw A (Lampert, Nickisch, and Harmeling 2009), and CIFAR100 (Krizhevsky, Hinton et al. 2009). |
| Dataset Splits | No | The paper describes a clustering task where the shared features are fed into the K-means algorithm for clustering. Performance is evaluated using standard metrics on the entire datasets, but no explicit training, validation, or testing splits are mentioned for these datasets within the context of the proposed method's experiments. |
| Hardware Specification | Yes | All tests are tuned best and accomplished on a desktop computer with a 2.10 GHz 13th Gen Intel(R) Core(TM) i7 Processor, 64 GB RAM, and MATLAB 2020b. |
| Software Dependencies | Yes | All tests are tuned best and accomplished on a desktop computer with a 2.10 GHz 13th Gen Intel(R) Core(TM) i7 Processor, 64 GB RAM, and MATLAB 2020b. |
| Experiment Setup | Yes | SLR-MVC has three parameters λ, γ, and L which control the weight on noise, the low-rank component, and the low-frequency component, respectively. We select these parameters by a brute-force search, with λ and γ ranging from {10 7, 10 6, 10 5, 10 4, 10 3, 10 2, 10 1} and L ranging from {2:2:40}. Initialize: µ1 = 5 10 4, µ2 = 10 3. The method converges when RE 10 5. µ1 = min(µ1 1.5, 1010), µ2 = min(µ2 1.5, 1010); L = min(L 1.5, N/2). |