5 citations · 13 across the 13 of their papers we have counts for
13 papers
Granule Description based on Compound Concepts
Jianqin Zhou, Sichun Yang, Xifeng Wang +1
Concise granule descriptions for definable granules and approaching descriptions for indefinable granules are challenging and important issues in granular computing. The concept wi…
Concept and Attribute Reduction Based on Rectangle Theory of Formal Concept
Jianqin Zhou, Sichun Yang, Xifeng Wang +1
Based on rectangle theory of formal concept and set covering theory, the concept reduction preserving binary relations is investigated in this paper. It is known that there are thr…
A New Algorithm based on Extent Bit-array for Computing Formal Concepts
Jianqin Zhou, Sichun Yang, Xifeng Wang +1
The emergence of Formal Concept Analysis (FCA) as a data analysis technique has increased the need for developing algorithms which can compute formal concepts quickly. The current…
iffDetector: Inference-aware Feature Filtering for Object Detection
Mingyuan Mao, Yuxin Tian, Baochang Zhang +4
Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature opt…
Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
Qilin Li, Wanquan Liu, Ling Li
Existing graph convolutional networks focus on the neighborhood aggregation scheme. When applied to semi-supervised learning, they often suffer from the overfitting problem as the…
The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation
Jintao Song, Huizhu Pan, Wuanquan Liu +2
In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Cha…
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Lu Tan, Ling Li, Wanquan Liu +2
Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working…
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Qilin Li, Senjian An, Ling Li +1
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph. It is…
Sparse Subspace Clustering via Diffusion Process
Qilin Li, Ling Li, Wanquan Liu
Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces. State-of-the-art subspace clustering methods are bas…
On the -error linear complexity for -periodic binary sequences via hypercube theory
Jianqin Zhou, Wanquan Liu, Guanglu Zhou
The linear complexity and the -error linear complexity of a binary sequence are important security measures for key stream strength. By studying binary sequences with the minimu…
Structure Analysis on the -error Linear Complexity for -periodic Binary Sequences
Jianqin Zhou, Wanquan Liu, Xifeng Wang
In this paper, in order to characterize the critical error linear complexity spectrum (CELCS) for -periodic binary sequences, we first propose a decomposition based on the cub…
On the -error linear complexity for -periodic binary sequences via Cube Theory
Jianqin Zhou, Wanquan Liu
The linear complexity and k-error linear complexity of a sequence have been used as important measures of keystream strength, hence designing a sequence with high linear complexity…
Characterization of -periodic binary sequences with fixed 3-error or 4-error linear complexity
Jianqin Zhou, Jun Liu, Wanquan Liu
The linear complexity and the -error linear complexity of a sequence have been used as important security measures for key stream sequence strength in linear feedback shift regi…