Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

12.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.6K
Sampling Plans01:23

Sampling Plans

255
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
255
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.2K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.2K
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

1.1K
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
1.1K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.9K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.9K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

516
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
516

You might also read

Related Articles

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

Sort by
Same author

Boundary-Aware Clustering of Spatial Transcriptomics Data Via Fourier Feature Mapping and Dynamic Self-Supervision.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Align Then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Game Theory Inspired Cross-View Interaction Alignment for Partially View-Aligned Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Balanced Multi-view Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Learning Disentangled Representations for Generalized Multi-view Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Advancing Room-Temperature Spin Qubits with Naphthalene Diimide-Based Chiral Covalent Organic Frameworks.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Sep 6, 2025

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

120

Graph regularized spatial-spectral subspace clustering for hyperspectral band selection.

Jun Wang1, Chang Tang1, Xiao Zheng2

  • 1School of Computer Science, China University of Geosciences, Wuhan 430074, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces GRSC, a novel graph regularized spatial-spectral subspace clustering method for hyperspectral band selection. GRSC effectively preserves spatial information and explores spectral correlations, outperforming existing methods in classification tasks.

Keywords:
ClusteringFeature learningHyperspectral band selectionSimilarity graph learning

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.7K

Related Experiment Videos

Last Updated: Sep 6, 2025

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

120
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.7K

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Data Science

Background:

  • Hyperspectral band selection is crucial for reducing data redundancy and noise in hyperspectral images (HSIs).
  • Existing clustering-based methods often overlook spatial information and regional variations within HSIs.
  • This limitation hinders optimal band selection performance.

Purpose of the Study:

  • To propose a graph regularized spatial-spectral subspace clustering (GRSC) method for hyperspectral band selection.
  • To address the limitations of previous methods by incorporating spatial information and regional characteristics.
  • To improve the accuracy and efficiency of hyperspectral band selection.

Main Methods:

  • Utilizing superpixel segmentation on the first principal component of HSIs to identify homogeneous regions and preserve spatial information.
  • Generating discriminative latent features from segmented regions to represent bands and mitigate noise effects.
  • Employing a self-representation subspace clustering model with l2,1-norm regularization to capture spectral correlations.
  • Learning an adaptive similarity graph between region-aware latent features to maintain spatial structure.

Main Results:

  • GRSC effectively preserves spatial information by segmenting HSIs into homogeneous regions.
  • The method mitigates noise by generating discriminative latent features.
  • GRSC demonstrates superior performance in hyperspectral image classification tasks compared to state-of-the-art methods across three public datasets.
  • The proposed method successfully explores spectral correlations and preserves spatial structures.

Conclusions:

  • GRSC offers a significant advancement in hyperspectral band selection by integrating spatial and spectral information effectively.
  • The method's ability to handle regional variations and noise makes it robust for HSI analysis.
  • The demonstrated effectiveness on public datasets highlights GRSC's potential for practical applications in remote sensing and related fields.