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Sampling hypergraphs via joint unbiased random walk
Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One...
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Patch area and uniform sampling on the surface of any ellipsoid
Algorithms for generating uniform random points on a triaxial ellipsoid are non-trivial to verify because of the non-analytical form of the surface...
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Random Subspace Sampling for Classification with Missing Data
Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully...
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Non-uniform Sampling-Based Breast Cancer Classification
The emergence of deep learning models and their remarkable success in visual object recognition and detection have fueled the medical imaging...
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Inclusive random sampling in graphs and networks
It is often of interest to sample vertices from a graph with a bias towards higher-degree vertices. One well-known method, which we call random...
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Solution sampling with random table constraints
Constraint programming provides generic techniques to efficiently solve combinatorial problems. In this paper, we tackle the natural question of...
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Practically Uniform Solution Sampling in Constraint Programming
The ability to sample solutions of a constrained combinatorial space has important applications in areas such as probabilistic reasoning and...
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Uniform and scalable sampling of highly configurable systems
Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These...
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Classification of arrhythmia disease through electrocardiogram signals using sampling vector random forest classifier
An electrocardiogram (ECG) is an electrical signal produced by ECG sensors to examine and visualize the heart’s functionality, quick identification...
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Cost-based analyses of random neighbor and derived sampling methods
Random neighbor sampling, or RN , is a method for sampling vertices with a mean degree greater than that of the graph. Instead of naïvely sampling a...
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Complex Network Hierarchical Sampling Method Combining Node Neighborhood Clustering Coefficient with Random Walk
Aiming at the problem of over-sampling for high-degree nodes and low-degree nodes in current sampling algorithms, a node Neighborhood Clustering...
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Fast Geometric Sampling for Phong-Like Reflection
Importance sampling is a critical technique for reducing the variance of Monte Carlo samples. However, the classical importance sampling based on the...
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Novel sampling method for the von Mises–Fisher distribution
The von Mises–Fisher distribution is a widely used probability model in directional statistics. An algorithm for generating pseudo-random vectors...
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Multi-stream network with key frame sampling for human action recognition
Human action recognition is a challenging task in the field of computer vision, where deep learning-based methods have made significant progress....
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Dimension Reduction Based on Sampling
Dimension reduction provides a powerful means of reducing the number of random variables under consideration. However, there were many similar tuples...
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Threshold Implementations with Non-uniform Inputs
Modern block ciphers designed for hardware and masked with Threshold Implementations (TIs) provide provable security against first-order attacks....
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Markov Chain Monte Carlo Sampling
The EM algorithm is suitable for topic models whose parameters and latent variables are distinguished. However, the approximate inference is usually...
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Good Negative Sampling for Triple Classification
Knowledge graphs are large and useful sources widely used for natural question answering, Web search and data analytics. They describe facts about a...