Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Demonstration of efficient predictive surrogates for large-scale quantum processors.

Nature communications·2026

Same author

A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.

Nature communications·2026

Same author

NoisePO: Efficient Semantic Noise Generation and Ranking for Diffusion-Based Text-to-Image Synthesis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Stability and Generalization for Distributed SGDA.

IEEE transactions on pattern analysis and machine intelligence·2026

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification.

Qingshan Liu, Yubao Sun, Cantian Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 24, 2017

Summary

This summary is machine-generated.

This study introduces an elastic net hypergraph learning model to overcome limitations in graph-based data analysis. The new model effectively captures high-order relationships for improved image clustering and classification.

More Related Videos

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Mar 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Machine Learning
Computer Vision
Data Mining

Background:

Graph models are effective for learning complex data structures.
Existing methods like l1-graph struggle with high-order relationships and correlated data.
Limitations exist in capturing nuanced data interdependencies.

Purpose of the Study:

To propose a novel elastic net hypergraph learning model.
To address the shortcomings of pairwise links in capturing high-order data relationships.
To enhance image clustering and classification by incorporating group effects and high-order dependencies.

Main Methods:

Developed a two-step elastic net hypergraph learning model.
Utilized a robust matrix elastic net for sample grouping (l1 constraint with l2 penalty).
Employed hypergraphs to represent high-order relationships and constructed a hypergraph Laplacian matrix.

Main Results:

The proposed model effectively captures high-order relationships missed by traditional graph methods.
Achieved improved performance in image clustering and multi-class semi-supervised classification.
Demonstrated effectiveness on face and handwriting databases.

Conclusions:

The elastic net hypergraph learning model offers a robust approach for complex data analysis.
It successfully overcomes limitations of existing graph-based methods.
The model shows significant potential for advancing image analysis tasks.