4 accepted papers
Ensemble clustering aims to derive a consensus partition from multiple base clustering results. Anchor-based methods construct compact similarity representations via anchors, substantially improving computational efficiency. However, when outliers contaminate the data, reconstructing the base cluste
Ensemble clustering learns a consensus result by integrating a set of base clustering results. Recently, anchor-based methods construct an anchor similarity matrix to represent the affinity relationships among samples, significantly improving computational efficiency. However, these methods struggle
Ensemble clustering learns more accurate consensus results from a set of weak base clustering results. This technique is more challenging than other clustering algorithms due to the base clustering result set's randomness and the inaccessibility of data features. Existing ensemble clustering methods…
In the autonomous driving industry, vehicle dynamic models are important to control-in-the-loop simulations. For current commercial self-driving simulators, vehicle dynamic models are expressed explicitly by sophisticated analytical equations, which are accurate but difficult to build and expensive…