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2007

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

8 years 8 months ago
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algori...
Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zha
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PAMI
Authors Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zhang, Qiang Yang, Stephen Lin
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