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Found 10 papers, 1 papers with code
Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack
Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are invariably affected by the plethora of redundant and erroneous edges present within the constructed graphs.
Improving Collaborative Filtering Recommendation via Graph Learning
Recommendation systems are designed to provide personalized predictions for items that are most appealing to individual customers.
Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point
Exploratory data analysis (EDA) is a vital procedure for data science projects.
Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression
This paper proposes a novel framework for accelerating support vector clustering.
Accelerate 3D Object Processing via Spectral Layout
Then we calculate the eigenvectors corresponding to the second and third smallest eigenvalues of its graph Laplacian and perform spectral layout to map each voxel into a pixel in 2D Cartesian coordinate plane.
Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation
Spectral clustering is one of the most popular clustering methods.
Graph Learning via Spectral Densification
Graph learning plays important role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, data clustering, and visualization, etc.
GRASPEL: Graph Spectral Learning at Scale
Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clustering, and visualization, etc.
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information.
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering.
