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Suppose we have an $n-dim$ time-series $X={x_1, x_2, \cdots, x_n}$ and we resample it to $m-dim$, $\hat{X}={\hat{x}_1, \hat{x}_2, \cdots, \hat{x}_m}$, where $m < n$.

Can we say this downsampling operation, always decrease the entropy, $H$, of the data: $H(\hat{X}) \leq H(X)$?

and, consequently, if $Y$ is a latent variable inferred from the data, the mutual information will also decrease: $I(Y,\hat{X})) \leq I(Y, X)$

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Yes, the entropy decreases because, $\hat{X}$ is a subset of $X$, and we have the general entropy rule: $H(X)=H(\hat{X})+H(X|\hat{X})$. This is general version of $H(X,Y)=H(X)+H(Y|X)$, for two RVs.

And, $H(Y|\hat{X})\geq H(Y|X)$ because we're conditioning on more RVs. Conditioning decreases entropy. Therefore, for the mutual information, we also have:

$$I(Y;\hat{X})=H(Y)-H(Y|\hat{X}) \leq H(Y)-H(Y|X)\leq I(Y;X)$$

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  • $\begingroup$ So we achieve the first equation because $H(\hat{X} | X) = 0$ ? right? $\endgroup$ – moh Sep 24 '18 at 13:02
  • $\begingroup$ Yep, it's like $H(X|X,Y)$ which is $0$ since $X$ is given. $\endgroup$ – gunes Sep 24 '18 at 13:16

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