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Dynamic Graph Learning for Dimension Reduction and Data Clustering [electronic resource] / by Lei Zhu, Jingjing Li, Zheng Zhang.

By: Contributor(s): Material type: TextSeries: Synthesis Lectures on Computer SciencePublisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XXI, 143 p. 41 illus., 40 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031423130
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • Q325.5-.7
Online resources:
Contents:
Introduction -- Dynamic Graph Learning for Feature Projection -- Dynamic Graph Learning for Feature Selection -- Dynamic Graph Learning for Data Clustering -- Research Frontiers.
In: Springer Nature eBookSummary: This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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Introduction -- Dynamic Graph Learning for Feature Projection -- Dynamic Graph Learning for Feature Selection -- Dynamic Graph Learning for Data Clustering -- Research Frontiers.

This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.

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