Research Frontiers

Lei Zhu; Jingjing Li; Zheng Zhang

doi:10.1007/978-3-031-42313-0_5

Research Frontiers

Lei Zhu^*
, Jingjing Li
, Zheng Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this book, we have delved into the realm of dynamic learning for dimension reduction and data clustering. Our exploration begins by highlighting the pressing need for a dynamic graph learning framework in the context of big data environments. We then delve into the challenges faced by dynamic graph learning when applied to dimension reduction and clustering tasks, including joint optimization, accurate modeling of data correlations with graphs, multi-view fusion, and out-of-sample extension.

Original language	English
Title of host publication	Synthesis Lectures on Computer Science
Publisher	Springer Nature
Pages	141-143
Number of pages	3
DOIs	https://doi.org/10.1007/978-3-031-42313-0_5
State	Published - 2024
Externally published	Yes

Publication series

Name	Synthesis Lectures on Computer Science
Volume	Part F1448
ISSN (Print)	1932-1228
ISSN (Electronic)	1932-1686

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10.1007/978-3-031-42313-0_5

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title = "Research Frontiers",

abstract = "In this book, we have delved into the realm of dynamic learning for dimension reduction and data clustering. Our exploration begins by highlighting the pressing need for a dynamic graph learning framework in the context of big data environments. We then delve into the challenges faced by dynamic graph learning when applied to dimension reduction and clustering tasks, including joint optimization, accurate modeling of data correlations with graphs, multi-view fusion, and out-of-sample extension.",

author = "Lei Zhu and Jingjing Li and Zheng Zhang",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

year = "2024",

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AU - Zhang, Zheng

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AB - In this book, we have delved into the realm of dynamic learning for dimension reduction and data clustering. Our exploration begins by highlighting the pressing need for a dynamic graph learning framework in the context of big data environments. We then delve into the challenges faced by dynamic graph learning when applied to dimension reduction and clustering tasks, including joint optimization, accurate modeling of data correlations with graphs, multi-view fusion, and out-of-sample extension.

UR - https://www.scopus.com/pages/publications/85172418442

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DO - 10.1007/978-3-031-42313-0_5

M3 - 章节

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BT - Synthesis Lectures on Computer Science

PB - Springer Nature

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Research Frontiers

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