Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Authors: Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the superiority of the proposed approach. |
| Researcher Affiliation | Academia | 1 School of Information Science and Engineering, Shandong Normal University, China 2 School of Computer Science and Technology, Shandong University, China 3 University of Electronic Science and Technology of China, China 4 School of Computing, National University of Singapore, Singapore |
| Pseudocode | Yes | The main steps for solving problem (4) are summarized in Algorithm 1. Algorithm 1 Multi-view feature selection via collaborative similarity structure learning with adaptive neighbors. Input: The pre-constructed similarity structures in v views {Sv}V v=1, the number of clusters k, the parameters α, β, γ. Output: The collaborative similarity structure S, the projection matrix P for feature selection, l identified features. 1: Initialize W with 1 V , the collaborative similarity structure S with the weighted sum of {Sv}V v=1. We also initialize F with the solution of problem (8) by substituting the Laplacian matrix calculated from the new S. 2: repeat 3: Update P with Eq.(7). 4: Update F by solving the problem in Eq.(8). 5: Update S with Eq.(13). 6: Update W with Eq.(17). 7: until Convergence Feature Selection 8: Calculate ||Pi ||2, (i = 1, 2, ..., d) and rank them in descending order. The l features with the top rank orders are finally determined as the features to be selected. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | 1) MSRC-v1 [Winn and Jojic, 2005]. 2) Handwritten Numeral [van Breukelen et al., 1998]. 3) Youtube [Liu et al., 2009]. 4) Outdoor Scene [Monadjemi et al., 2002]. |
| Dataset Splits | No | The paper mentions that 'Each experiment is performed 50 times and the mean results are reported' but does not specify the exact training, validation, or test dataset splits (e.g., percentages or sample counts), nor does it describe cross-validation setup explicitly for each dataset. It relies on standard datasets but does not explicitly state how they were split for these experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python, PyTorch, TensorFlow versions, or solver versions). |
| Experiment Setup | No | The paper mentions that parameters α, β, γ were chosen from a range and adjusted, but it does not explicitly state the specific hyperparameter values used for the reported results. Other experimental setup details such as learning rates, batch sizes, or optimizer settings are not provided. |