Embedded Feature Selection on Graph-Based Multi-View Clustering
Authors: Wenhui Zhao, Guangfei Li, Haizhou Yang, Quanxue Gao, Qianqian Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches. |
| Researcher Affiliation | Academia | Wenhui Zhao1, Guangfei Li1, Haizhou Yang1, Quanxue Gao1*, Qianqian Wang1 1 School of Telecommunication Engineering, Xidian University, Shaanxi 710071, China |
| Pseudocode | Yes | Algorithm 1: EFSGMC Input: Data matrices: {Xv}V v=1 RN dv, anchors number M, and cluster number K. Output: Cluster assignment matrix e G with K classes. 1: Initialize W = J = 0, Kv = Mv = 0, ρ0, ρ1, ρ2, pho ρ = 1.1, max ρi = 1010, αv = 1 V , γ, λ, β. 2: while not converg do 3: Update Qv by using (15) 4: Update Hv by using (30) 5: Update J by using (34) 6: Update Gv by solving (36) 7: Update Pv by solving (37) 8: Update αv by using (41) 9: Update Kv, Mv, W and ρi by using (42), (43), (44) and (45), respectively; 10: Directly achieve the K clusters based on the cluster assignment matrix e G=PV v=1 Gv αv ; 11: end while 12: return Clustering results. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Datasets As shown in Table 1, MSRC (Winn and Jojic 2005), HW (Dua and Graff 2017), Mnist4 (Deng 2012), Cal101-20 (Fei-Fei, Fergus, and Perona 2007) and Reuters (Apt e, Damerau, and Weiss 1994) are selected. |
| Dataset Splits | No | The paper mentions hyperparameters are selected in a range to obtain optimal results, but it does not provide specific details on training, validation, or test dataset splits (percentages, counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its 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 | Settings The hyperparameters are selected in the range of [0.0001, 0.001, 0.01, 0.1, 0.5, 1, 5, 10, 50, 100, 1000, 10000] to obtain the optimal results. Adjusting K within the range of 2-10, the optimal value of 5 was chosen. |