Let be non-empty, open and proper. This paper is concerned with , the space of -integrable Borel measures on equipped with the transportation metric introduced by Figalli and Gigli that allows the creation and destruction of mass on . Alternatively, we show that is isometric to a subset of Borel measures with the ordinary Wasserstein distance, on the one point completion of equipped with the shortcut metric In this article we construct bi-Lipschitz embeddings of the set of unordered -tuples in into Hilbert space. This generalises Almgren's bi-Lipschitz embedding theorem to the setting of optimal partial transport.
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http://dx.doi.org/10.1007/s00208-024-02831-x | DOI Listing |
Math Ann
March 2024
Division of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA UK.
Let be non-empty, open and proper. This paper is concerned with , the space of -integrable Borel measures on equipped with the transportation metric introduced by Figalli and Gigli that allows the creation and destruction of mass on . Alternatively, we show that is isometric to a subset of Borel measures with the ordinary Wasserstein distance, on the one point completion of equipped with the shortcut metric In this article we construct bi-Lipschitz embeddings of the set of unordered -tuples in into Hilbert space.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
November 2022
Cross-modal hashing, favored for its effectiveness and efficiency, has received wide attention to facilitating efficient retrieval across different modalities. Nevertheless, most existing methods do not sufficiently exploit the discriminative power of semantic information when learning the hash codes while often involving time-consuming training procedure for handling the large-scale dataset. To tackle these issues, we formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data, so as to minimize the quantization loss of mapping such data to hamming space and propose an efficient fast discriminative discrete hashing (FDDH) approach for large-scale cross-modal retrieval.
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