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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).  To solve this,  Where  
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