Maximum Mean Discrepancy
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A Maximum Mean Discrepancy is a statistic of an embedded distribution difference between the source domain with sufficient but finite labeled data and the target domain with sufficient unlabeled data.
- AKA: MMD.
- See: Reproducing Kernel Hilbert Space.
References
2009
- (Chen et al., 2009) ⇒ Bo Chen, Wai Lam, Ivor Tsang, and Tak-Lam Wong. (2009). “Extracting Discrimininative Concepts for Domain Adaptation in Text Mining.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557045
- … Maximum Mean Discrepancy (MMD) [5] is adopted to measure the embedded distribution difference between the source domain with sufficient but finite labeled data and the target domain with sufficient unlabeled data.
2007
- (Gretton et al., 2007) ⇒ A. Gretton, K. Borgwardt, M. Rasch, Bernhard Schölkopf, and Alexander J. Smola. (2007). “A Kernel Method for the Two-Sample Problem.” In: Advances in Neural Information Processing Systems, 19.
- … We call this statistic the Maximum Mean Discrepancy (MMD). ...
2006
- (Borgwardt et al., 2006) ⇒ Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J. Smola. (2006). “Integrating structured biological data by kernel maximum mean discrepancy." Bioinformatics, 22(14).