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• Rana Basheer

# Alpha Divergence

Updated: Apr 2

On several occasions you want to classify an observation x into one of two categories

Depending on which ever probability is higher we classify x as either being in

However, there is a non-zero probability that x might belong to Bayes Error

Larger the area under this region there is a greater chance for misclassification. The area under this error region is given by $A=\int{min\left(P\left(C_1|x)\right),P\left(C_2|x\right)\right)p(x) dx}$

Since minimum function is quite difficult to deal with it is replaced by a smooth power function as $min\left(a,b\right)\le a^\alpha b^{1-\alpha},\forall \alpha \in \left(0,1\right)$

Hence the upper bound for the area is given by $A \le \int P\left(C_1|x\right)^\alpha P\left(C_2|x\right)^{1-\alpha} p(x) dx =P\left(C_1\right)^\alpha P\left(C_2\right)^{1-\alpha}C_\alpha \left(P\left(x|C_1\right)||P\left(x|C_2\right)\right) dx$ $C_\alpha \left(P\left(x|C_1\right)||P\left(x|C_2\right)\right)=\int P\left(x|C_1\right)^\alpha P\left(x|C_2\right)^{1-\alpha}$

The $D_\alpha=-log\left(C_\alpha\left(\cdot||\cdot\right)\right)$

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