arxiv_ml 85% Match Research Paper Machine Learning Researchers,Image Analysis Specialists,Data Scientists,Engineers working with spectroscopic or imaging data 4 days ago

Manifold Learning for Hyperspectral Images

graph-neural-networks › graph-learning

📄 Abstract

Abstract: Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results.

Authors (5)

Fethi Harkat
EDP, DT

Guillaume Gey
DT

Valérie Perrier
EDP

Kévin Polisano
SVH

Tiphaine Deuberet
DT

Institutions

🏛️ EDP, DT 🏛️ DT 🏛️ EDP 🏛️ SVH

Submitted

March 19, 2025

arXiv Category

cs.CV

Whispers, Nov 2025, Barcelana, Spain

arXiv PDF

Key Contributions

Proposes a manifold learning approach using UMAP to construct adjacency graphs for Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This method captures nonlinear correlations, preserves global structure, enhances feature separability, and significantly improves the performance of subsequent machine learning algorithms.

Business Value

Enables more accurate analysis and decision-making from complex imaging data, leading to better quality control, diagnostics, or material characterization.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Moderate. Requires implementation of the UMAP-based graph construction and integration with existing ML pipelines.

Limitations Addressed

Traditional techniques like PCA struggle with the complex topology of XRT ME images, limiting neural network performance.

Performance Gains

Significantly improving the performance of machine learning algorithms.

Technical Tags

Manifold LearningHyperspectral ImagesFeature ExtractionX-Ray TransmissionUniform Manifold Approximation and Projection (UMAP)Adjacency GraphsNonlinear CorrelationsMachine LearningClassificationData Topology

Research Topics

Dimensionality ReductionFeature Representation LearningImage AnalysisGraph-based LearningNonlinear Data Structures

Methods & Architectures

Uniform Manifold Approximation and Projection (UMAP)Adjacency Graph ConstructionManifold Learning Neural Networks (for classification)

Applications & Tasks

Image Processing Spectroscopy Materials Science Medical Imaging (potentially) Improving feature extraction for complex image dataCapturing nonlinear data structuresEnhancing machine learning performance on HSI Feature ExtractionDimensionality ReductionImage Classification

Related Fields

Machine LearningComputer VisionData MiningTopologyGraph Theory

Keywords

Manifold LearningHyperspectral ImagingUMAPFeature ExtractionGraph ConstructionX-Ray TransmissionNonlinear Dimensionality ReductionImage ClassificationData Topology

Academic Context

#Dimensionality Reduction#Feature Representation Learning#Image Analysis#Graph-based Learning#Nonlinear Data Structures

Technology Stack

Frameworks & Libraries

UMAP

Commercial Potential

Potential Products

Advanced image analysis softwareTools for material characterizationEnhanced diagnostic imaging systems

Target Industries

ManufacturingHealthcareMaterials ScienceAerospaceResearch & Development

Use Case Examples

Classifying different materials based on their hyperspectral X-ray signaturesImproving defect detection in manufactured goodsAnalyzing complex spectral data for scientific research

Competitive Edge

Offers a more robust approach to feature extraction for complex, nonlinear data compared to linear methods like PCA.

Market Opportunity

Growing market for advanced image analysis and data interpretation tools.

Revenue Models

Software licensingspecialized consulting services.

Resource Requirements

Compute Needs

Moderate to High, depending on dataset size and UMAP parameters.

Data Requirements

High-dimensional image data (e.g., hyperspectral images).

Deployment Constraints

Computational cost of UMAP and graph construction for very large datasets.

Scalability

UMAP's scalability can be a concern for extremely large datasets, though approximations exist.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years for integration into specialized analysis tools.

Patent Potential

Low, focuses on applying existing manifold learning techniques.

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