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Found 11 papers, 1 papers with code
A Novel Exploration of Diffusion Process based on Multi-types Galton-Watson Forests
With the two-phase setting of the MGWF, one can interpret the diffusion process and the Google PageRank system explicitly.
Concept and Attribute Reduction Based on Rectangle Theory of Formal Concept
Second, we derive the bounds for both the maximum number of relative necessary concepts and the maximum number of unnecessary concepts and it is a difficult problem as either in concept reduction preserving binary relations or attribute reduction of decision formal contexts, the computation of formal contexts from formal concepts is a challenging problem.
Granule Description based on Compound Concepts
Concise granule descriptions for definable granules and approaching descriptions for indefinable granules are challenging and important issues in granular computing.
A New Algorithm based on Extent Bit-array for Computing Formal Concepts
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.
Sleep Staging Based on Multi Scale Dual Attention Network
In the literature, a large number of sleep staging methods based on single-channel EEG have been proposed with promising results and achieve the preliminary automation of sleep staging.
iffDetector: Inference-aware Feature Filtering for Object Detection
In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages.
Using the Split Bregman Algorithm to Solve the Self-repelling Snake Model
Preserving contour topology during image segmentation is useful in many practical scenarios.
Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
Existing graph convolutional networks focus on the neighborhood aggregation scheme.
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
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 well for noisy images.
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph.
Sparse Subspace Clustering via Diffusion Process
Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces.
