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Data representation for learning and...

Rajapakse, Vinodh Nalin.

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Data representation for learning and information fusion in bioinformatics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data representation for learning and information fusion in bioinformatics./
作者:	Rajapakse, Vinodh Nalin.
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
標題:	Applied Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3599545
ISBN:	9781303489198

Data representation for learning and information fusion in bioinformatics.
Rajapakse, Vinodh Nalin.

Data representation for learning and information fusion in bioinformatics. - 150 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2013.

This thesis deals with the rigorous application of nonlinear dimension reduction and data organization techniques to biomedical data analysis. The Laplacian Eigenmaps algorithm is representative of these methods and has been widely applied in manifold learning and related areas. While their asymptotic manifold recovery behavior has been well-characterized, the clustering properties of Laplacian embeddings with finite data are largely motivated by heuristic arguments. We develop a precise bound, characterizing cluster structure preservation under Laplacian embeddings. From this foundation, we introduce flexible and mathematically well-founded approaches for information fusion and feature representation. These methods are applied to three substantial case studies in bioinformatics, illustrating their capacity to extract scientifically valuable information from complex data.

ISBN: 9781303489198Subjects--Topical Terms:

1669109
Applied Mathematics.

Data representation for learning and information fusion in bioinformatics.
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520 $a This thesis deals with the rigorous application of nonlinear dimension reduction and data organization techniques to biomedical data analysis. The Laplacian Eigenmaps algorithm is representative of these methods and has been widely applied in manifold learning and related areas. While their asymptotic manifold recovery behavior has been well-characterized, the clustering properties of Laplacian embeddings with finite data are largely motivated by heuristic arguments. We develop a precise bound, characterizing cluster structure preservation under Laplacian embeddings. From this foundation, we introduce flexible and mathematically well-founded approaches for information fusion and feature representation. These methods are applied to three substantial case studies in bioinformatics, illustrating their capacity to extract scientifically valuable information from complex data.
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