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Exploring the Frontier of Graph-base...

Chen, Bohan.

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Exploring the Frontier of Graph-based Approaches for Image and Document Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring the Frontier of Graph-based Approaches for Image and Document Analysis./
Author:	Chen, Bohan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	225 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Applied mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31302280
ISBN:	9798382785912

Exploring the Frontier of Graph-based Approaches for Image and Document Analysis.
Chen, Bohan.

Exploring the Frontier of Graph-based Approaches for Image and Document Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 225 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Graph-based machine learning is a powerful framework for analyzing and understanding complex data structures in various domains. This thesis introduces novel graph-based methods in multiple image analysis tasks, including classification, segmentation, and unmixing, as well as their application in enhancing large language models. The key contributions include: (1) the development of new core-set selection and batch active learning methods that significantly improve the efficiency of graph-based active learning while maintaining its effectiveness; (2) the integration of graph learning, active learning, and advanced feature embedding methods to construct pipelines for SAR image classification and multi- or hyperspectral image segmentation, outperforming neural network-based classifiers or segmenters in semi-supervised learning tasks with limited training data; (3) the incorporation of graph-based regularization into the optimization problem of hyperspectral unmixing, enabling the utilization of a small amount of labeled pixels to greatly improve the performance compared to blind unmixing; and (4) the extension of graph Laplacian-based methods to automatically construct knowledge graphs in combination with large language models, enhancing their information retrieval and response generation capabilities.The proposed methods showcase the effectiveness and versatility of graph-based approaches in addressing challenges such as limited labeled data, computational efficiency, and knowledge representation. The thesis demonstrates the potential of graph-based methods in pushing the boundaries of image and document analysis and their applicability in a wide range of machine learning problems.

ISBN: 9798382785912Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Active learning

Exploring the Frontier of Graph-based Approaches for Image and Document Analysis.
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300 $a 225 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Bertozzi, Andrea L.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
520 $a Graph-based machine learning is a powerful framework for analyzing and understanding complex data structures in various domains. This thesis introduces novel graph-based methods in multiple image analysis tasks, including classification, segmentation, and unmixing, as well as their application in enhancing large language models. The key contributions include: (1) the development of new core-set selection and batch active learning methods that significantly improve the efficiency of graph-based active learning while maintaining its effectiveness; (2) the integration of graph learning, active learning, and advanced feature embedding methods to construct pipelines for SAR image classification and multi- or hyperspectral image segmentation, outperforming neural network-based classifiers or segmenters in semi-supervised learning tasks with limited training data; (3) the incorporation of graph-based regularization into the optimization problem of hyperspectral unmixing, enabling the utilization of a small amount of labeled pixels to greatly improve the performance compared to blind unmixing; and (4) the extension of graph Laplacian-based methods to automatically construct knowledge graphs in combination with large language models, enhancing their information retrieval and response generation capabilities.The proposed methods showcase the effectiveness and versatility of graph-based approaches in addressing challenges such as limited labeled data, computational efficiency, and knowledge representation. The thesis demonstrates the potential of graph-based methods in pushing the boundaries of image and document analysis and their applicability in a wide range of machine learning problems.
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