常量和矩阵转换技巧¶
在此整理一下常量和矩阵转换的技巧，以备查阅。
二次型¶
\[
\sum_i^n \sum_j^n x_i x_j \mathbf{Q}_{i, j}=\mathbf{x}^T \mathbf{Q} \mathbf{x}
\]
协方差矩阵¶
\(\mathbf{C}_{ij}\) 是 \(\mathbf{X}\) 的第 \(i\) 个样本和第 \(j\) 个样本的协方差（假设 \(\mathbf{X}\) 已去过均值）
\[
\mathbf{C}_{i, j}=\frac{1}{N} \sum_k^n \mathbf{X}_{i, k} \mathbf{X}_{j, k}
\]
我们可以发现
\[
\left[\mathbf{x}_k \mathbf{x}_k^T\right]_{i, j}=\mathbf{X}_{i, k} \mathbf{X}_{j, k}
\]
同时 \(\mathbf{C}_{i,j} = \frac{1}{N}\sum_k^n \mathbf{X}_{i,k}\mathbf{X}_{j,k}\) 还可以看作是 \(\mathbf{X}\) 的第 \(i\) 个样本和第 \(j\) 个样本的内积，所以
\[
\mathbf{C}=\frac{1}{N} \mathbf{X} \mathbf{X}^T
\]
向量求导¶
[2]
一阶¶
\[
\frac{\partial a^T x}{\partial x} = \frac{\partial x^T a}{\partial x} = a
\]
二阶¶
\[
\frac{\partial x^T B x}{\partial x} = (B + B^T) x
\]
练习¶
证明
\[
\begin{gathered}
\underset{a \in \mathbb{R}^m}{\min} \frac{1}{2} \| x - D\alpha \|_2^2 + \lambda \| \alpha \|_2^2, \quad x \in \mathbb{R}^n, D\in\mathbb{R}^{n\times m} \\
\Rightarrow \alpha= (DD^T + \lambda I)^{-1}D^T x
\end{gathered}
\]
矩阵的迹¶
定义¶
\[
\operatorname{Tr}(A) = \sum_{i=1}^n a_{ii}, \quad A = (a_{ij}) \in \mathbb{R}^{n\times n}.
\]
性质¶
\[
\begin{gathered}
\text{for } A, B, C\in \mathbb{R}^{n\times n}, a \in \mathbb{R}\\
\|A\|_F^2 = \sum_{i=1}^n\sum_{j=1}^n a_{ij}^2 = \operatorname{Tr}(A^T A), \\
\operatorname{Tr}(A) = \operatorname{Tr}(A^T), \\
\operatorname{Tr}(A+B) = \operatorname{Tr}(B+A), \\
\operatorname{Tr}(aA) = a\operatorname{Tr}(A), \\
\operatorname{Tr}(AB) = \operatorname{Tr}(BA), \\
\operatorname{Tr}(ABC) = \operatorname{Tr}(BCA) = \operatorname{Tr}(CAB).
\end{gathered}
\]
迹的导数¶
First order¶
\[
\begin{gathered}
\frac{\partial}{\partial X} \operatorname{Tr}(XA) = A^T \\
\operatorname{Tr}(X^T A) = A
\end{gathered}
\]
Second order¶
\[
\begin{gathered}
\frac{\partial}{\partial X}\operatorname{Tr}(X^T X A) = XA^T + XA \\
\frac{\partial}{\partial X}\operatorname{Tr}(X^T B X) = B^T X + B X \\
\end{gathered}
\]
练习¶
\[
\begin{gathered}
\underset{A \in \mathbf{R}^{k\times m}}{\min}\| X - D A\|_F^2 + \lambda \| A \|_F^2, \quad X \in \mathbb{R}^{n\times m}, D \in \mathbb{n\times k} \\
\Rightarrow A = (D^TD+\lambda I)^{-1}D^TX
\end{gathered}
\]
Reference¶
[1] https://zhuanlan.zhihu.com/p/411057937
[2] CSE 902: Selected Topics in Recognition by Machine, Anil Jain, Michigan State University.
最后更新:
2022-12-27