Name
Abstract | ||
|---|---|---|
Real-world datasets often describe data instances in different views that complement information for each other. Unfortunately, synthesizing these views for learning a comprehensive description of data items is challenging. To tackle it, many approaches have been studied to explore correlations between various features by assuming that all views can be projected into a same semantic subspace. Following this idea, we propose a novel semi-supervised method, namely dual graphregularized multi-view feature learning (DGMFL), for data representation in this paper. The core idea is to generate a latent subspace among different views. Our approach utilizes dual graph regularization to capture semantic relationships among data items on both multi-view features and label information, as well as locates view-specific features for each view to reduce the effects of uncorrelated items. In this way, DGMFL could achieve more comprehensive representations hidden in multi-view datasets. Extensive experiments demonstrate that DGMFL model is superior to state-of-the-art multi-view learning methods on real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/HPCC/SmartCity/DSS.2018.00066 | 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) |
Keywords | Field | DocType |
noise reduction,multi-view data,dual graph regularization | Noise reduction,External Data Representation,Subspace topology,Computer science,Uncorrelated,Theoretical computer science,Regularization (mathematics),Dual graph,Feature learning,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-6615-9 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhikui Chen | 1 | 692 | 66.76 |
| Xiru Qiu | 2 | 0 | 0.68 |
| Liang Zhao | 3 | 49 | 2.65 |
| Jianing Du | 4 | 0 | 1.01 |