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A Scalable Dual Approach to Semidefinite Metric Learning

Shen, Chunhua; Kim, Junae; Wang, Lei


Distance metric learning plays an important role in many vision problems. Previous work of quadratic Maha-lanobis metric learning usually needs to solve a semidefinite programming (SDP) problem. A standard interior-point SDP solver has a complexity of O(D6.5) (with D the dimension of input data), and can only solve problems up to a few thousand variables. Since the number of variables is D(D +l)/2, this corresponds to a limit around D < 100. This high complexity hampers the application of...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
Source: Graph connectivity in sparse subspace clustering
DOI: 10.1109/CVPR.2011.5995447


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