Maximum Mean Discrepancy MMD
Tags: #machine learning #mmdEquation
$$\textup{MMD}(\mathbb{F},X,Y):=\sup_{f \in\mathbb{F}}(\frac{1}{m}\sum_{i=1}^{m}f(x_{i}) -\frac{1}{n}\sum_{j=1}^{n}f(y_{j}))$$Latex Code
\textup{MMD}(\mathbb{F},X,Y):=\sup_{f \in\mathbb{F}}(\frac{1}{m}\sum_{i=1}^{m}f(x_{i}) -\frac{1}{n}\sum_{j=1}^{n}f(y_{j}))
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Introduction
Equation
Latex Code
\textup{MMD}(\mathbb{F},X,Y):=\sup_{f \in\mathbb{F}}(\frac{1}{m}\sum_{i=1}^{m}f(x_{i}) -\frac{1}{n}\sum_{j=1}^{n}f(y_{j}))
Explanation
Latex code for the Maximum Mean Discrepancy MMD. I will briefly introduce the notations in this formulation.
: Superior of the discrepancy measure between two distribution.
: Mean of probability distribution X with m data points.
: Mean of probability distribution Y with n data points.
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