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TCA: IEEE TRANSACTIONS ON NEURAL NETWORKS(IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011)
2019-09-23 17:30:59
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# Background We have $(X_s,Y_s)$ and $X_t$, but $X_s, X_t$ are not from the same ditribution, i.e. $P(Y_s|X_s) \neq P(Y_t|X_t)$. To solve this, we use the following method. # Assumption There exist a transformation $\phi$ that $P(Y_s|\phi(X_s)) \approx P(Y_t|\phi(X_t))$. # Solutioin To find $\phi$, we try to minimize following distance called MMD(maximum mean discrepancy). ![title](https://leanote.com/api/file/getImage?fileId=5d88a3a3ab64411f16000c3f) To solve this, ![title](https://leanote.com/api/file/getImage?fileId=5d89ac6fab64411f1600171c) Where ![title](https://leanote.com/api/file/getImage?fileId=5d89ad05ab6441211d0016b6) ![title](https://leanote.com/api/file/getImage?fileId=5d89ad20ab64411f16001728)
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