Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Embedded Feature Selection on Graph-Based Multi-View Clustering
Authors: Wenhui Zhao, Guangfei Li, Haizhou Yang, Quanxue Gao, Qianqian Wang
AAAI 2024 | Venue PDF | 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. |