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]
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.