- 多次元尺度法
- \(d_{ij}^2\)の計算
- \(\displaystyle \sum_{i=1}^nd_{ij}^2,\;\sum_{j=1}^nd_{ij}^2\)の計算
- \(\displaystyle \sum_{i=1}^n\sum_{j=1}^nd_{ij}^2\)の計算
- \(\boldsymbol{x}_i^{\text{T} }\boldsymbol{x_j}\)の計算
- \(\text{X}\text{X}^{\text{T}}\)の計算
- 正準相関分析
- 線形結合後の\(\boldsymbol{a}^{\text{T} }\boldsymbol{x},\boldsymbol{b}^{\text{T} }\boldsymbol{y} \)の分散の導出
- 線形結合後の\(\boldsymbol{a}^{\text{T} }\boldsymbol{x}\)と\(\boldsymbol{b}^{\text{T} }\boldsymbol{y} \)の間の共分散の導出
- \(S_{xy}S_{yy}^{-1}S_{xy}^{\text{T}}\boldsymbol{a}=\lambda^2S_{xx}\boldsymbol{a}\)の導出
- \(S_{xx}^{-\frac{1}{2} }S_{xy}S_{yy}^{-1}S_{xy}^{\text{T}}S_{xx}^{-\frac{1}{2} }\boldsymbol{c}=\lambda^2\boldsymbol{c}\)の導出
- \(\boldsymbol{a}=S_{xx}^{-\frac{1}{2}}\boldsymbol{c}, \boldsymbol{b}=\frac{1}{\sqrt{\eta_1}}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a} \)が\(\boldsymbol{a}^{\text{T}}S_{xx}\boldsymbol{a}=\boldsymbol{b}^{\text{T}}S_{yy}\boldsymbol{b}=1\)の条件を満たすこと
- \(\lambda=\boldsymbol{a}^{\text{T} }S_{xy}\boldsymbol{b}\)が最大値\(\sqrt{\eta_1}\)を満たすこと
統計学実践ワークブックの行間埋め 第26章
\begin{eqnarray}
\|\boldsymbol{x}_i-\boldsymbol{x}_j\|^2
&=&
(\boldsymbol{x}_i-\boldsymbol{x}_j)^{\text{T} }(\boldsymbol{x}_i-\boldsymbol{x}_j)&& \\ \\
&=&
\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_i-\boldsymbol{x}_j^{\text{T} }\boldsymbol{x}_i -\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j+\boldsymbol{x}_j^{\text{T} }\boldsymbol{x}_j& \\ \\
&=&
\| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-(\boldsymbol{x}_j^{\text{T} }\boldsymbol{x}_i)^{\text{T}} -\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&&...\text{スカラーであるため、転置をとっても同じ値になる} \\ \\
&=&
\| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j -\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&& \\ \\
&=&
\| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&& \\ \\
\end{eqnarray}
ここでは\(\displaystyle \sum_{i=1}^nd_{ij}^2\)の計算について示す。
\begin{eqnarray}
\displaystyle \sum_{i=1}^nd_{ij}^2
&=&
\displaystyle \sum_{i=1}^n \| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&& \\ \\
&=&
a+n\|\boldsymbol{x}_j\|^2-2\displaystyle \sum_{i=1}^n \boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&& \\ \\
&=&
a+n\|\boldsymbol{x}_j\|^2-2\left(\displaystyle \sum_{i=1}^n \boldsymbol{x}_i^{\text{T} } \right)\boldsymbol{x}_j&& \\ \\
&=&
a+n\|\boldsymbol{x}_j\|^2-2 \;\boldsymbol{0} ^{\text{T} }\boldsymbol{x}_j&&...p.232より\displaystyle \sum_{i=1}^n \boldsymbol{x}_i=0 \\ \\
&=&
a+n\|\boldsymbol{x}_j\|^2
\end{eqnarray}
\begin{eqnarray}
\displaystyle \sum_{i=1}^n\sum_{j=1}^nd_{ij}^2
&=&
\displaystyle \sum_{i=1}^n\sum_{j=1}^n \| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&& \\ \\
&=&
\displaystyle \sum_{i=1}^n\left(\sum_{j=1}^n \| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j\right)&& \\ \\
&=&
\displaystyle \sum_{i=1}^n\left( a+n\|\boldsymbol{x}_j\|^2 \right)&& \\ \\
&=&
an+\displaystyle \sum_{i=1}^n\left( n\|\boldsymbol{x}_j\|^2 \right)&& \\ \\
&=&
an+n\displaystyle \sum_{i=1}^n\|\boldsymbol{x}_j\|^2 && \\ \\
&=&
an+na && \\ \\
&=&
2na
\end{eqnarray}
\begin{eqnarray}
&&d_{ij}^2&=&\| \boldsymbol{x}_i\|^2+\|\boldsymbol{x}_j\|^2-2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j \\ \\
&\Leftrightarrow& 2\boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&=&-d_{ij}^2+&\| \boldsymbol{x}_i\|^2&+&\|\boldsymbol{x}_j\|^2& \\ \\
&& &=&-d_{ij}^2+&\frac{1}{n}\left( \displaystyle \sum_{j=1}^n d_{ij}^2 -a\right)&+&\frac{1}{n}\left( \sum_{i=1}^n d_{ij}^2 -a\right)& \\ \\
&& &=&-d_{ij}^2+&\frac{1}{n}\left( \displaystyle \sum_{j=1}^n d_{ij}^2 \right)&+&\frac{1}{n}\left( \sum_{i=1}^n d_{ij}^2 \right)&-&\frac{2a}{n}& \\ \\
&& &=&-d_{ij}^2+&\frac{1}{n}\left( \displaystyle \sum_{j=1}^n d_{ij}^2 \right)&+&\frac{1}{n}\left( \sum_{i=1}^n d_{ij}^2 \right)&-&\frac{1}{n}\left( \frac{1}{n}\sum_{i=1}^n\sum_{j=1}^n d_{ij}^2 \right)&\;&...2na=\sum_{i=1}^n\sum_{j=1}^nd_{ij}^2より& \\ \\
&\Leftrightarrow& \boldsymbol{x}_i^{\text{T} }\boldsymbol{x}_j&=&-\frac{1}{2}(d_{ij}^2+&\frac{1}{n}\left( \displaystyle \sum_{j=1}^n d_{ij}^2 \right)&+&\frac{1}{n}\left( \sum_{i=1}^n d_{ij}^2 \right)&-&\frac{1}{n}\left( \frac{1}{n}\sum_{i=1}^n\sum_{j=1}^n d_{ij}^2 \right)&) \\ \\
\end{eqnarray}
\begin{eqnarray}
-2\text{X}\text{X}^{\text{T}}&=&
-2\left(
\begin{array}{cccc}
\boldsymbol{x}_1^{\text{T}} \\
\boldsymbol{x}_2^{\text{T}} \\
\vdots \\
\boldsymbol{x}_n^{\text{T}}
\end{array}
\right)
\left(\boldsymbol{x}_1,\boldsymbol{x}_2,\ldots,\boldsymbol{x}_n\right) \\ \\
&=&
-2\left(
\begin{array}{cccc}
\boldsymbol{x}_1^{\text{T}}\boldsymbol{x}_1 & \boldsymbol{x}_1^{\text{T}}\boldsymbol{x}_2 & \ldots & \boldsymbol{x}_1^{\text{T}}\boldsymbol{x}_n \\
\boldsymbol{x}_2^{\text{T}}\boldsymbol{x}_1 & \boldsymbol{x}_2^{\text{T}}\boldsymbol{x}_2 & \ldots & \boldsymbol{x}_2^{\text{T}}\boldsymbol{x}_n \\
\vdots & \vdots & \ddots & \vdots \\
\boldsymbol{x}_n^{\text{T}}\boldsymbol{x}_1 & \boldsymbol{x}_n^{\text{T}}\boldsymbol{x}_2 & \ldots & \boldsymbol{x}_n^{\text{T}}\boldsymbol{x}_n
\end{array}
\right) \\ \\
&=&
\left(
\begin{array}{cccc}
d_{11}^2 & d_{12}^2 & \ldots & d_{1n}^2 \\
d_{21}^2 & d_{22}^2 & \ldots & d_{2n}^2 \\
\vdots & \vdots & \ddots & \vdots \\
d_{n1}^2 & d_{n2}^2 & \ldots & d_{nn}^2
\end{array}
\right)
-\frac{1}{n}\displaystyle \sum_{i=1}^n
\left(
\begin{array}{cccc}
d_{i1}^2 & d_{i2}^2 & \ldots & d_{in}^2 \\
d_{i1}^2 & d_{i2}^2 & \ldots & d_{in}^2 \\
\vdots & \vdots & \ddots & \vdots \\
d_{i1}^2 & d_{i2}^2 & \ldots & d_{in}^2 \\
\end{array}
\right)
-\frac{1}{n}\displaystyle \sum_{j=1}^n
\left(
\begin{array}{cccc}
d_{1j}^2 & d_{1j}^2 & \ldots & d_{1j}^2 \\
d_{2j}^2 & d_{2j}^2 & \ldots & d_{2j}^2 \\
\vdots & \vdots & \ddots & \vdots \\
d_{nj}^2 & d_{nj}^2 & \ldots & d_{nj}^2 \\
\end{array}
\right)
+\frac{1}{n^2}\displaystyle \sum_{i=1}^n\sum_{j=1}^n d_{ij}
\left(
\begin{array}{cccc}
1 & 1 & \ldots & 1 \\
1 & 1 & \ldots & 1 \\
\vdots & \vdots & \ddots & \vdots \\
1 & 1 & \ldots & 1 \\
\end{array}
\right) \\ \\
&=&
\text{D}
-\frac{1}{n}
\left(
\begin{array}{cccc}
1 & 1 & \ldots & 1 \\
1 & 1 & \ldots & 1 \\
\vdots & \vdots & \ddots & \vdots \\
1 & 1 & \ldots & 1 \\
\end{array}
\right) \text{D}
-\frac{1}{n}
\text{D}
\left(
\begin{array}{cccc}
1 & 1 & \ldots & 1 \\
1 & 1 & \ldots & 1 \\
\vdots & \vdots & \ddots & \vdots \\
1 & 1 & \ldots & 1 \\
\end{array}
\right)
+\frac{1}{n^2}
\left(
\begin{array}{cccc}
1 & 1 & \ldots & 1 \\
1 & 1 & \ldots & 1 \\
\vdots & \vdots & \ddots & \vdots \\
1 & 1 & \ldots & 1 \\
\end{array}
\right)
\text{D}
\left(
\begin{array}{cccc}
1 & 1 & \ldots & 1 \\
1 & 1 & \ldots & 1 \\
\vdots & \vdots & \ddots & \vdots \\
1 & 1 & \ldots & 1 \\
\end{array}
\right)
\\ \\
&=&
\text{D}
-\frac{1}{n}
\text{J}_n \text{D}
-\frac{1}{n}
\text{D}
\text{J}_n
+\frac{1}{n^2}
\text{J}_n\text{D}\text{J}_n \\ \\
&=&
\text{I}_n\text{D}\text{I}_n
-\frac{1}{n}
\text{J}_n \text{D}\text{I}_n
-\frac{1}{n}\text{I}_n
\text{D}
\text{J}_n
+\frac{1}{n^2}
\text{J}_n\text{D}\text{J}_n \\ \\
&=&
(\text{I}_n-\frac{1}{n}\text{J}_n)
\text{D}(\text{I}_n-\frac{1}{n}\text{J}_n) \\ \\
\Leftrightarrow\text{X}\text{X}^{\text{T}}&=&-\frac{1}{2}(\text{I}_n-\frac{1}{n}\text{J}_n)
\text{D}(\text{I}_n-\frac{1}{n}\text{J}_n)
\end{eqnarray}
\(\boldsymbol{x}\)について計算する。ここで、
\begin{eqnarray}
E[\boldsymbol{x}]=\boldsymbol{0}
\end{eqnarray}
とする。そうすることで、
\begin{eqnarray}
E[\boldsymbol{a}^{\text{T}}\boldsymbol{x}]=\boldsymbol{0}
\end{eqnarray}
となる。分散を求めるにあたって、\(\text{X}=(\boldsymbol{x}_1,\boldsymbol{x}_2,\ldots,\boldsymbol{x}_n)^{\text{T}}\)とすると、
\begin{eqnarray}
S_{xx}=\frac{1}{n-1}\text{X}^{\text{T} }\text{X}
\end{eqnarray}
である(p.193より)。期待値が0なので、\(X=X_C\)と見なせ、\(\boldsymbol{a}^{\text{T}}\boldsymbol{x}\)の分散を計算すると
\begin{eqnarray}
\frac{1}{n-1}(\text{X}\boldsymbol{a})^{\text{T} }(\text{X}\boldsymbol{a})
&=&
\boldsymbol{a}^{\text{T} }\frac{1}{n-1}\text{X}^{\text{T} }\text{X}\boldsymbol{a} \\ \\
&=&
\boldsymbol{a}^{\text{T} }S_{xx}\boldsymbol{a}
\end{eqnarray}
同様の計算が\(\boldsymbol{y}\)についても行えるため、\(\boldsymbol{b}^{\text{T} }\boldsymbol{y}\)の分散は\(\boldsymbol{b}^{\text{T} }S_{yy}\boldsymbol{b}\)
\begin{eqnarray}
E[\boldsymbol{x}]&=&\boldsymbol{0} \\ \\
E[\boldsymbol{y}]&=&\boldsymbol{0}
\end{eqnarray}
とする。そうすることで、
\begin{eqnarray}
E[\boldsymbol{a}^{\text{T}}\boldsymbol{x}]=\boldsymbol{0} \\ \\
E[\boldsymbol{b}^{\text{T}}\boldsymbol{y}]=\boldsymbol{0}
\end{eqnarray}
となる。共分散を求めるにあたって、
\begin{eqnarray}
\text{X}=(\boldsymbol{x}_1,\boldsymbol{x}_2,\ldots,\boldsymbol{x}_n)^{\text{T}} \\ \\
\text{Y}=(\boldsymbol{y}_1,\boldsymbol{y}_2,\ldots,\boldsymbol{y}_n)^{\text{T}}
\end{eqnarray}
とする。これらの共分散を計算すると
\begin{eqnarray}
S_{xy}=\frac{1}{n-1}\text{X}^{\text{T} }\text{Y}
\end{eqnarray}
であるから、これを利用して、
\begin{eqnarray}
\frac{1}{n-1}(\text{X}\boldsymbol{a})^{\text{T} }(\text{Y}\boldsymbol{b})
&=&
\boldsymbol{a}^{\text{T} }\frac{1}{n-1}\text{X}^{\text{T} }\text{Y}\boldsymbol{b} \\ \\
&=&
\boldsymbol{a}^{\text{T} }S_{xy}\boldsymbol{b}
\end{eqnarray}
が得られる。
\begin{eqnarray}
&&\left\{
\begin{array}{l}
S_{xy}\boldsymbol{b}-\lambda S_{xx}\boldsymbol{a}=0 \\
S_{xy}^{\text{T} }\boldsymbol{a}-\lambda S_{yy}\boldsymbol{b}=0
\end{array}
\right.\\ \\
&\Leftrightarrow&
\left\{
\begin{array}{l}
S_{xy}\boldsymbol{b}-\lambda S_{xx}\boldsymbol{a}=0 \\
\frac{1}{\lambda}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a}=\boldsymbol{b}
\end{array}
\right.\\ \\
&\Rightarrow&
S_{xy}(\frac{1}{\lambda}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a})-\lambda S_{xx}\boldsymbol{a}=0 \\ \\
&\Rightarrow&
S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a}=\lambda^2 S_{xx}\boldsymbol{a} \\ \\
\end{eqnarray}
\(S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a}=\lambda^2 S_{xx}\boldsymbol{a}\)に\(\boldsymbol{c}=S_{xx}^{\frac{1}{2} }\boldsymbol{a}\)を代入する。
\begin{eqnarray}
&&&S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }&\boldsymbol{a}&=&\lambda^2 S_{xx}\boldsymbol{a} \\ \\
&\Leftrightarrow&
S_{xx}^{-\frac{1}{2}}&S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }&(S_{xx}^{-\frac{1}{2}}S_{xx}^{\frac{1}{2}})\boldsymbol{a}&=&S_{xx}^{-\frac{1}{2}}\lambda^2S_{xx}\boldsymbol{a} \\ \\
&\Leftrightarrow&
S_{xx}^{-\frac{1}{2}}&S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }&S_{xx}^{-\frac{1}{2}}S_{xx}^{\frac{1}{2}}\boldsymbol{a}&=&\lambda^2S_{xx}^{\frac{1}{2}}\boldsymbol{a} \\ \\
&\Leftrightarrow&
S_{xx}^{-\frac{1}{2}}&S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }&S_{xx}^{-\frac{1}{2}}\boldsymbol{c}&=&\lambda^2\boldsymbol{c} \\ \\
\end{eqnarray}
p.236より、\( \|\boldsymbol{c}\|^2=1 \)であることを用いる。\(\boldsymbol{a}^{\text{T}}S_{xx}\boldsymbol{a}\)を求めると
\begin{eqnarray}
\boldsymbol{a}^{\text{T}}S_{xx}\boldsymbol{a}
&=&
&(S_{xx}^{-\frac{1}{2}}\boldsymbol{c})^{\text{T} }&S_{xx}S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
&\boldsymbol{c}^{\text{T} }(S_{xx}^{-\frac{1}{2}})^{\text{T} }&S_{xx}^{\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
&\boldsymbol{c}^{\text{T} }(S_{xx}^{\text{T} })^{-\frac{1}{2}}&S_{xx}^{\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
&\boldsymbol{c}^{\text{T} }S_{xx}^{-\frac{1}{2}}&S_{xx}^{\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
&\boldsymbol{c}^{\text{T} }\boldsymbol{c} \\ \\
&=&
&\|\boldsymbol{c}\|^2 \\ \\
&=&
&1 \\ \\
\end{eqnarray}
が得られる。次に\(\boldsymbol{b}^{\text{T}}S_{yy}\boldsymbol{b}\)を求める。
\begin{eqnarray}
\boldsymbol{b}^{\text{T}}S_{yy}\boldsymbol{b}
&=&
(\frac{1}{\sqrt{\eta_1}}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a})^{\text{T}}&S_{yy}&\frac{1}{\sqrt{\eta_1}}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}(S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a})^{\text{T}}&S_{yy}&S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{a}^{\text{T}}S_{xy}(S_{yy}^{-1})^{\text{T}}&S_{yy}S_{yy}^{-1}&S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{a}^{\text{T}}S_{xy}(S_{yy}^{\text{T}})^{-1}&1&S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{a}^{\text{T}}S_{xy}S_{yy}^{-1}&1&S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{a}^{\text{T}}S_{xy}&S_{yy}^{-1}&S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\eta_1}(S_{xx}^{-\frac{1}{2}}\boldsymbol{c})^{\text{T}}S_{xy}&S_{yy}^{-1}&S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{c}^{\text{T}}(S_{xx}^{-\frac{1}{2}})^{\text{T}}S_{xy}&S_{yy}^{-1}&S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{c}^{\text{T}}(S_{xx}^{\text{T}})^{-\frac{1}{2}}S_{xy}&S_{yy}^{-1}&S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\eta_1}\boldsymbol{c}^{\text{T}}S_{xx}^{-\frac{1}{2}}S_{xy}&S_{yy}^{-1}&S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\eta_1}&\boldsymbol{c}^{\text{T}}&(S_{xx}^{-\frac{1}{2}}S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c}) \\ \\
&=&
\frac{1}{\eta_1}&\boldsymbol{c}^{\text{T}}&\lambda^2\boldsymbol{c}&S_{xx}^{-\frac{1}{2}}&S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }&S_{xx}^{-\frac{1}{2}}\boldsymbol{c}&=&\lambda^2\boldsymbol{c}より \\ \\
&=&
\frac{1}{\eta_1}&\boldsymbol{c}^{\text{T}}&\eta_1\boldsymbol{c} \\ \\
&=&
&\boldsymbol{c}^{\text{T} }\boldsymbol{c} \\ \\
&=&
&\|\boldsymbol{c}\|^2 \\ \\
&=&
&1 \\ \\
\end{eqnarray}
\begin{eqnarray}
\boldsymbol{a}^{\text{T}}S_{xy}\boldsymbol{b}
&=&
(S_{xx}^{-\frac{1}{2}}\boldsymbol{c})^{\text{T}}S_{xy}\frac{1}{\sqrt{\eta_1}}S_{yy}^{-1}S_{xy}^{\text{T} }\boldsymbol{a} \\ \\
&=&
\frac{1}{\sqrt{\eta_1}}\boldsymbol{c}^{\text{T}}(S_{xx}^{-\frac{1}{2}})^{\text{T}}S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }(S_{xx}^{-\frac{1}{2}}\boldsymbol{c}) \\ \\
&=&
\frac{1}{\sqrt{\eta_1}}\boldsymbol{c}^{\text{T}}(S_{xx}^{\text{T}})^{-\frac{1}{2}}S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\sqrt{\eta_1}}\boldsymbol{c}^{\text{T}}S_{xx}^{-\frac{1}{2}}S_{xy}S_{yy}^{-1}S_{xy}^{\text{T} }S_{xx}^{-\frac{1}{2}}\boldsymbol{c} \\ \\
&=&
\frac{1}{\sqrt{\eta_1}}\boldsymbol{c}^{\text{T}}\lambda^2\boldsymbol{c} \\ \\
&=&
\frac{1}{\sqrt{\eta_1}}\boldsymbol{c}^{\text{T}}\eta\boldsymbol{c} \\ \\
&=&
\sqrt{\eta_1}\boldsymbol{c}^{\text{T}}\boldsymbol{c} \\ \\
&=&
\sqrt{\eta_1} \\ \\
\end{eqnarray}