[Machine Learning Linear Regression] The inner product and L2 norm in the loss function - Code World

[Machine Learning Linear Regression] The inner product and L2 norm in the loss function

Enterprise 2023-04-09 20:59:23 views: null

Properties: $\rangle \mathbf{u}, \mathbf{v} \rangle$

L2 norm： $\mathbf{y-Xw-b} ||$ 或 $\mathbf{y-Xw-b}||_2$

In mathematics, it is usually considered that when $p = 2$ , $L_p$ The norm represents the Euclidean norm (also known as $L_2$ norm), because at this time $L_p$ The norm is calculated in the same way as the Euclidean distance. Therefore, in the expression $L_2$ Norm, the exponent 2 is usually omitted, directly written $\mathbf{x} ||$ or $\mathbf{x}||_2$ 。当 $\neq$ , you need to explicitly specify $L_p$ The exponent of the norm.

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