no code implementations • 21 Mar 2021 • Ruqian Lu, Shuhan Zhang
While there have been lots of work studying frequent subgraph mining, very rare publications have discussed frequent subnet mining from more complicated data structures such as Petri nets.
no code implementations • 28 Jan 2021 • Ruqian Lu, Shuhan Zhang
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets.
no code implementations • 21 Oct 2019 • Ruqian Lu, Shengluan Hou
SSMRBL is an incremental learning process that can learn more than one representation, which is an appropriate solution for dealing with the scarce of labeled training data in the age of big data and with the heavy workload of learning compound structured labels.
no code implementations • 14 Oct 2019 • Shengluan Hou, Ruqian Lu
Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora.
no code implementations • 3 Sep 2019 • Ruqian Lu, Shengluan Hou, Chuanqing Wang, Yu Huang, Chaoqun Fei, Songmao Zhang
We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine.
no code implementations • 28 Mar 2017 • Yangyang Li, Ruqian Lu
Our work is based on the fact that the set of all SPD matrices with the same size has a Lie group structure, and we aim to transform the manifold learning to the SPD matrix Lie group.
no code implementations • 3 Mar 2017 • Yangyang Li, Ruqian Lu
Traditional manifold learning algorithms often bear an assumption that the local neighborhood of any point on embedded manifold is roughly equal to the tangent space at that point without considering the curvature.