| 1. |
- Huang, Chen, et al.
(author)
-
Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation
- 2021
-
In: 2021 IEEE International Conference on Data Mining (ICDM). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 200-209
-
Conference paper (peer-reviewed)abstract
- Semi-supervised learning aims to generate a model with a better performance using plenty of unlabeled data. However, most existing methods treat unlabeled data equally without considering whether it is safe or not, which may lead to the degradation of prediction performance. In this paper, towards reliable semi-supervised learning, we propose a data-driven algorithm, called Reliability Propagation (RP), to learn the reliability of each unlabeled instance. The basic idea is to take local label regularity as a prior, and then perform reliability propagation on an adaptive graph. As a result, the most reliable unlabeled instances could be selected to construct a safer classifier. Beyond, the distributed RP algorithm is introduced to scale up to large volumes of data. In contrast to existing approaches, RP exploits the structural information and shed light on the soft instance selection for unlabeled data in a classifier-independent way. Experiments on both synthetic and real-world data have demonstrated that RP allows extracting most reliable unlabeled instances and supports a gained prediction performance compared to other algorithms.
|
|
| 2. |
- Yang, Qinli, et al.
(author)
-
A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale
- 2019
-
In: Environmental Modelling and Software. - : Elsevier BV. - 1364-8152. ; 122
-
Journal article (peer-reviewed)abstract
- Most spatiotemporal studies treat spatial and temporal analysis separately. However, spatial and temporal changes occur simultaneously and are correlated. In this study, we propose a generic framework to simultaneously analyse the spatial and temporal variations of water quality on a catchment scale. Specifically, we analyse the heterogeneity of temporal evolution of water quality data among different sampling sites, and the heterogeneity of spatial distribution of water quality data over different sampling times, respectively, by integrating the techniques of normalized mutual information, dynamic time wrapping and cluster analysis. To bring deep insight into the spatiotemporal variations, inter-change and intra-change are further defined and distinguished, respectively. Taking the Fuxi River catchment as a case study, results indicate that the proposed framework is intuitive and efficient. Beyond this, the generic framework can be expanded for other catchments and various environmental data.
|
|
