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The yiy_{i}yi’s minimizing the MSE distortion given a partition {R1,…,RN}\{ R_{1},\dots,R_{N} \}{R1,…,RN} are uniquely given by yi=E[X∣X∈Ri], i=1,…,Ny_{i}=E[X|X\in R_{i}], \ i=1,\dots,Nyi=E[X∣X∈Ri], i=1,…,N
The yiy_{i}yi’s minimizing the MSE Vector Quantizer given a partition {R1,…,RN}\{ R_{1},\dots,R_{N} \}{R1,…,RN} are uniquely given by ci=E[X∣X∈Ri], i=1,…,N\mathbf{c}_{i}=E[\mathbf{X}|\mathbf{X}\in R_{i}], \ i=1,\dots,Nci=E[X∣X∈Ri], i=1,…,N
If we have that X∼fX\sim fX∼f, then fX∣Ri(x)={f(x)P(X∈Ri)if x∈Ri0otherwisef_{X|R_{i}}(x)=\begin{cases} \frac{f(x)}{P(X\in R_{i})} & \text{if }x\in R_{i} \\ 0 & \text{otherwise} \end{cases}fX∣Ri(x)={P(X∈Ri)f(x)0if x∈Riotherwiseso E[X∣X∈Ri]=∫−∞∞xfX∣Ri(x) dx=∫Rixf(x) dx∫Rif(x) dx=∫Rixf(x) dxP(X∈Ri)E[X|X\in R_{i}]=\int\limits _{-\infty}^{\infty}xf_{X|R_{i}}(x) \, dx =\frac{ \int\limits _{R_{i}}xf(x) \, dx }{\int\limits_{R_{i}} f(x) \, dx }=\frac{ \int\limits _{R_{i}}xf(x) \, dx }{P(X\in R_{i})}E[X∣X∈Ri]=−∞∫∞xfX∣Ri(x)dx=Ri∫f(x)dxRi∫xf(x)dx=P(X∈Ri)Ri∫xf(x)dx