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Fast Graph Laplacian regularized kernel learning via semidefinite-quadratic-linear programming.

Wu, Xiaoming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 30-34). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Preliminaries --- p.4 / Chapter 2.1 --- Kernel Learning Theory --- p.4 / Chapter 2.1.1 --- Positive Semidefinite Kernel --- p.4 / Chapter 2.1.2 --- The Reproducing Kernel Map --- p.6 / Chapter 2.1.3 --- Kernel Tricks --- p.7 / Chapter 2.2 --- Spectral Graph Theory --- p.8 / Chapter 2.2.1 --- Graph Laplacian --- p.8 / Chapter 2.2.2 --- Eigenvectors of Graph Laplacian --- p.9 / Chapter 2.3 --- Convex Optimization --- p.10 / Chapter 2.3.1 --- From Linear to Conic Programming --- p.11 / Chapter 2.3.2 --- Second-Order Cone Programming --- p.12 / Chapter 2.3.3 --- Semidefinite Programming --- p.12 / Chapter 3 --- Fast Graph Laplacian Regularized Kernel Learning --- p.14 / Chapter 3.1 --- The Problems --- p.14 / Chapter 3.1.1 --- MVU --- p.16 / Chapter 3.1.2 --- PCP --- p.17 / Chapter 3.1.3 --- Low-Rank Approximation: from SDP to QSDP --- p.18 / Chapter 3.2 --- Previous Approach: from QSDP to SDP --- p.20 / Chapter 3.3 --- Our Formulation: from QSDP to SQLP --- p.21 / Chapter 3.4 --- Experimental Results --- p.23 / Chapter 3.4.1 --- The Results --- p.25 / Chapter 4 --- Conclusion --- p.28 / Bibliography --- p.30

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_327377
Date January 2011
ContributorsWu, Xiaoming., Chinese University of Hong Kong Graduate School. Division of Information Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, viii, 34 p. : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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