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[Notes on Mathematics for ESL] Chapter 2: Overview of Supervised Learning

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2.4 Statistical Decision Theory

Derivation of Equation (2.16)

The expected predicted error (EPE) under the squared error loss:

Taking derivatives with respect to $\beta$:

In order to minimize the EFE, we make derivatives equal zero which gives Equation (2.16):

Note: $x^T\beta$ is a scalar, and $\beta$ is a constant.

2.5 Local Methods in High Dimensions

Intuition on Equation (2.24)

There are $N$ $p$-dimensional data point $x_1,\dots, x_N$, that is, $N\times p$ dimensions in total. Let $r_i=\Vert x_i \Vert$. Without loss of generality, we assume that $A < r_1 < \dots < r_n < 1$. Let $U(A)$ be the region of all possible sampled data which meet the assumptation:

The goal is to find $A$ such that $U(A)=\frac12U(0)$. It turns out to be a integration problem on a $N \times p$ dimensional space.

With some mathematical techniques (which make me overwhelmed), we can get $U(A)=(1-A^p)^N$. Then $U(0)=1$. Solving $(1-A^p)^N=1/2$, we obtain Equation (2.24):

Derivation of Equation (2.27) and (2.28)

The variation is over all training sets $\mathcal{T}$, and over all values of $y_0$, while keeping $x_0$ fixed. Note that $x_0$ and $y_0$ are chosen independently of $\mathcal{T}$ and so the expectations commute: $\mathrm{E}_{y_0\vert x_0}\mathrm{E}_{\mathcal{T}}=\mathrm{E}_{\mathcal{T}}\mathrm{E}_{y_0 \vert x_0}$. Also $\mathrm{E}_\mathcal{T}=\mathrm{E}_\mathcal{X}\mathrm{E}_{\mathcal{Y \vert X}}$.

In order to make the derivation more comprehensible, here lists some definitions:

$y_0-\hat y_0$ can be written as the sum of three terms:

Following above definitions, we have $U_1=\varepsilon$, $U_3=0$. In addition, clearly we have $\mathrm{E}_\mathcal{T}U_2=0$. When squaring $U_1-U_2-U_3$, we can eliminate all three cross terms and one squared terms $U_3^2$.

Following the definition of variance, we have: $\mathrm{E}_{y_0\vert x_0}\mathrm{E}_\mathcal{T}U_1^2=\mathrm{Var}(\varepsilon)=\sigma^2$ and $\mathrm{E}_\mathcal{T}(\hat y_0 - \mathrm{E}_\mathcal{T}\hat y_0)^2=\mathrm{Var}_\mathcal{T}(\hat y_0)$.

Since $U_2=\sum_{i=1}^Nl_i(x_0)\varepsilon_i$, we have $\mathrm{Var}_\mathcal{T}(\hat y_0)=\mathrm{E}_\mathcal{T}U_2^2$ as

Since $\mathrm{E}_\mathcal{T}\varepsilon\varepsilon^T=\sigma^2I_N$, this is equal to $\mathrm{E}_\mathcal{T}x_0(X^TX)^{-1}x_0\sigma^2$. This completes the derivation of Equation (2.27).

Under the conditions stated by the authors, $X^TX/N$ is then approximately equal to $\mathrm{Cov}(X)=\mathrm{Cov}(x_0)$. Applying $\mathrm{E}_{x_0}$ to $\mathrm{E}_\mathcal{T}x_0(X^TX)^{-1}x_0\sigma^2$, we obtain (approximately)

This completes the derivation of Equation (2.28).