Name
Abstract | ||
|---|---|---|
Unsupervised multi-view feature selection (UMV-FS) deals with the dimension reduction problem wherein instances are unlabeled and represented by heterogeneous features. Existing mainstream UMV-FS methods incorporate instance-wise view interactions based on graphs to guide feature selection, in which within-view selection decisions are independently learned to piece up a global feature subset. However, this strategy induces a globally sub-optimal feature selection decision in the sense that unexpected redundant features across views proliferate. Furthermore, existing studies are performed in view-complete frameworks, which hardly satisfies real-world applications. To address these issues, we propose a novel cross-view feature selection (CVFS) framework in an unsupervised manner, which can process large-scale/streaming data. This is the first attempt to approach incomplete multi-view feature selection by devising and fusing two-wise view interactions. Specifically, we incorporate the traditional instance-wise view interactions based on graphs to find discriminative features in each view and model a novel kind of feature-wise view interactions to enforce selection diversity and reduce feature redundancy. These techniques can yield a globally optimal feature subset across all views. Comprehensive experiments validate the effectiveness and efficiency of the proposed CVFS. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.knosys.2021.107595 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Multi-view feature selection,Unsupervised learning,Incomplete views,Feature redundancy,View diversity | Journal | 234 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Xu | 1 | 31 | 6.79 |
| Yu Yin | 2 | 0 | 0.34 |
| Jun Wang | 3 | 0 | 0.34 |
| Jinmao Wei | 4 | 23 | 6.46 |
| Jian Liu | 5 | 0 | 1.01 |
| Lina Yao | 6 | 981 | 93.63 |
| Wenjie Zhang | 7 | 1616 | 105.67 |