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Adaptive Data Representation and Ana...

Xu, Jieren.

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Adaptive Data Representation and Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Adaptive Data Representation and Analysis./
Author:	Xu, Jieren.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	167 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10837819
ISBN:	9780438377202

Adaptive Data Representation and Analysis.
Xu, Jieren.

Adaptive Data Representation and Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 167 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: B.

Thesis (Ph.D.)--Duke University, 2018.

This item is not available from ProQuest Dissertations & Theses.

This dissertation introduces and analyzes algorithms that aim to adaptively handle complex datasets arising in the real-world applications. It contains two major parts. The first part describes an adaptive model of 1-dimensional signals that lies in the field of adaptive time-frequency analysis. It explains a current state-of-the-art work, named the Synchrosqueezed transform, in this field. Then it illustrates two proposed algorithms that use non-parametric regression to reveal the underlying oscillatory patterns of the targeted 1-dimensional signal, as well as to estimate the instantaneous information, e.g., instantaneous frequency, phase, or amplitude functions, by a statistical pattern driven model. The second part proposes a population-based imaging technique for human brain bundle/connectivity recovery. It applies local streamlines as novelly adopted learning/testing features to segment the brain white matter and thus reconstruct the whole brain information. It also develops a module, named as the streamline diffusion filtering, to improve the streamline sampling procedure. Even though these two parts are not related directly, they both rely on an alignment step to register the latent variables to some coordinate system and thus to facilitate the final inference. Numerical results are shown to validate all the proposed algorithms.

ISBN: 9780438377202Subjects--Topical Terms:

1669109
Applied Mathematics.

Adaptive Data Representation and Analysis.
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300 $a 167 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Xu, Jieren.
502 $a Thesis (Ph.D.)--Duke University, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation introduces and analyzes algorithms that aim to adaptively handle complex datasets arising in the real-world applications. It contains two major parts. The first part describes an adaptive model of 1-dimensional signals that lies in the field of adaptive time-frequency analysis. It explains a current state-of-the-art work, named the Synchrosqueezed transform, in this field. Then it illustrates two proposed algorithms that use non-parametric regression to reveal the underlying oscillatory patterns of the targeted 1-dimensional signal, as well as to estimate the instantaneous information, e.g., instantaneous frequency, phase, or amplitude functions, by a statistical pattern driven model. The second part proposes a population-based imaging technique for human brain bundle/connectivity recovery. It applies local streamlines as novelly adopted learning/testing features to segment the brain white matter and thus reconstruct the whole brain information. It also develops a module, named as the streamline diffusion filtering, to improve the streamline sampling procedure. Even though these two parts are not related directly, they both rely on an alignment step to register the latent variables to some coordinate system and thus to facilitate the final inference. Numerical results are shown to validate all the proposed algorithms.
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