Learning Meta-Distance for Sequences by Learning a Ground Metric via Virtual Sequence Regression
Distance between sequences is structural by nature because it needs to establish the temporal alignments among the temporally correlated vectors in sequences with varying lengths. Generally, distances for sequences heavily depend on the ground metric between the vectors in sequences to infer the ali...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 1 vom: 15. Jan., Seite 286-301 |
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| Format: | Article en ligne |
| Langue: | English |
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2022
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Accès en ligne |
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