Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data
Authors: Yanyong Huang, Zongxin Shen, Tianrui Li, Fengmao Lv
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 ExperimentsDatasets. We evaluate the performance of the proposed UNIFIER on six real-world multi-view datasets, including two text datasets: BBCSport [Wen et al., 2019] and BBC4views [Chen et al., 2021]; a face image dataset: Yale [Liu et al., 2021a]; two object image datasets: Aloi [Rocha and Goldenstein, 2013] and Caltech101-20 [Huang et al., 2019]; and a handwritten digit image dataset: USPS2View [Liu et al., 2016]. A detailed description of the datasets is summarized in Table 1. |
| Researcher Affiliation | Academia | 1Joint Laboratory of Data Science and Business Intelligence, School of Statistics, Southwestern University of Finance and Economics, Chengdu, China 2School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China |
| Pseudocode | Yes | Algorithm 1 Iterative Algorithm of UNIFIER |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology. |
| Open Datasets | Yes | Datasets. We evaluate the performance of the proposed UNIFIER on six real-world multi-view datasets, including two text datasets: BBCSport [Wen et al., 2019] and BBC4views [Chen et al., 2021]; a face image dataset: Yale [Liu et al., 2021a]; two object image datasets: Aloi [Rocha and Goldenstein, 2013] and Caltech101-20 [Huang et al., 2019]; and a handwritten digit image dataset: USPS2View [Liu et al., 2016]. |
| Dataset Splits | No | The paper mentions varying the missing data ratio and feature selection ratio, and using clustering performance to evaluate, but does not provide specific training/validation/test dataset splits. It states: 'We adopt a commonly used approach to assess MUFS [Tang et al., 2021; Zhang et al., 2019], employing clustering performance to evaluate the quality of selected features. In this paper, we run the graph-based multi-view clustering algorithm (GMC) [Wang et al., 2019] 30 times on the selected features and use two widely recognized metrics, namely clustering accuracy (ACC) and normalized mutual information (NMI), to measure performance.' |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies. |
| Experiment Setup | Yes | The parameters α(v) and λ of our method are set within the range of {10 3, 10 2, 10 1, 1, 10, 102, 103}. To simplify, the weight parameters for each view are set to be the same and the scale parameter γv of Geman-Mc Clure function is set to 1. As determining the optimal number of selected features is still a challenging problem, we vary the proportion of selected features from {0.1, 0.2, 0.3, 0.4, 0.5}. We adopt a commonly used approach to assess MUFS [Tang et al., 2021; Zhang et al., 2019], employing clustering performance to evaluate the quality of selected features. |