Joint Optimization of Fidelity and Commensurability for Manifold Alignment and Graph Matching

dimensionality reduction;multiview learning;dissimilarity representation;graph matching;canonical correlational analysis;procrustes analysis;Applied Mathematics & Statistics

Adali, SancarNaiman, Daniel Q. ;

Johns Hopkins University

关键词: dimensionality reduction;    multiview learning;    dissimilarity representation;    graph matching;    canonical correlational analysis;    procrustes analysis;    Applied Mathematics & Statistics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37006/ADALI-DISSERTATION-2014.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

In this thesis, we investigate how to perform inference in settings in which the data consist of different modalities or views. For effective learning utilizing the information available, data fusion that considers all views of these multiview data settings is needed. We also require dimensionality reduction to address the problems associated with high dimensionality, or ;;the curse of dimensionality.” We are interested in the type of information that is available in the multiview data that is essential for the inference task. We also seek to determine the principles to be used throughout the dimensionality reduction and data fusion steps to provide acceptable task performance. Our research focuses on exploring how these queries and their solutions are relevant to particular data problems of interest.