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Visual Exploration of High-Dimension...

Liu, Shusen.

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Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections./
作者:	Liu, Shusen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268395
ISBN:	9781369780130

Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections.
Liu, Shusen.

Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 150 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--The University of Utah, 2017.

With the ever-increasing amount of available computing resources and sensing devices, a wide variety of high-dimensional datasets are being produced in numerous fields. The complexity and increasing popularity of these data have led to new challenges and opportunities in visualization.

ISBN: 9781369780130Subjects--Topical Terms:

523869
Computer science.

Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections.
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245 1 0 $a Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections.
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500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Valerio Pascucci.
502 $a Thesis (Ph.D.)--The University of Utah, 2017.
520 $a With the ever-increasing amount of available computing resources and sensing devices, a wide variety of high-dimensional datasets are being produced in numerous fields. The complexity and increasing popularity of these data have led to new challenges and opportunities in visualization.
520 $a Since most display devices are limited to communication through two-dimensional (2D) images, many visualization methods rely on 2D projections to express high-dimensional information. Such a reduction of dimension leads to an explosion in the number of 2D representations required to visualize high-dimensional spaces, each giving a glimpse of the high-dimensional information. As a result, one of the most important challenges in visualizing high-dimensional datasets is the automatic filtration and summarization of the large exploration space consisting of all 2D projections. In this dissertation, a new type of algorithm is introduced to reduce the exploration space that identifies a small set of projections that capture the intrinsic structure of high-dimensional data. In addition, a general framework for summarizing the structure of quality measures in the space of all linear 2D projections is presented.
520 $a However, identifying the representative or informative projections is only part of the challenge. Due to the high-dimensional nature of these datasets, obtaining insights and arriving at conclusions based solely on 2D representations are limited and prone to error. How to interpret the inaccuracies and resolve the ambiguity in the 2D projections is the other half of the puzzle. This dissertation introduces projection distortion error measures and interactive manipulation schemes that allow the understanding of high-dimensional structures via data manipulation in 2D projections.
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