Ex. 3.14

Ex. 3.14

Show that in the orthogonal case, PLS stops after \(m=1\) steps, because subsequent \(\hat\varphi_{mj}\) in Step 2 in Algorithm 3.3 are zero.

Soln. 3.14

Observe that in Algorithm 3.3 on partial least squares (PLS) if \(\hat\varphi_{mj}=0\) for all \(j\) on any given step \(m\) then the algorithm will stop.

We start with \(m=1\). We have

(a)

\[\begin{equation} \bb{z}_1 = \sum_{j=1}^p\hat\varphi_{1j}\bx^{(0)}_j \text{ with } \hat\varphi_{1j} = \langle \bx^{(0)}_j, \by \rangle. \non \end{equation}\]

(b) For \(\hat\theta_1\), we have

\[\begin{eqnarray} \hat\theta_1 = \langle \bb{z}_1, \by \rangle/\langle \bb{z}_1, \bb{z}_1 \rangle\non \end{eqnarray}\]

where

\[\begin{eqnarray} \langle \bb{z}_1, \bb{z}_1 \rangle &=& \left\langle \sum_{j=1}^p\hat\varphi_{1j}\bx^{(0)}_j, \sum_{j=1}^p\hat\varphi_{1j}\bx_{j}^{(0)}\right\rangle\non\\ &=& \sum_{i=1}^p\sum_{j=1}^p\hat\varphi_{1i}\hat\varphi_{1j}\langle \bx^{(0)}_j, \bx^{(0)}_{j'} \rangle\non\\ &=& \sum_{i=1}^p\sum_{j=1}^p\hat\varphi_{1i}\hat\varphi_{1j}\delta_{ij}\non\\ &=&\sum_{i=1}^p\hat\varphi_{1i}^2\non \end{eqnarray}\]

since \(\bx_i^{(0)}\) are orthogonal. On the other hand, we have

\[\begin{equation} \langle \bb{z}_1, \by \rangle = \sum_{i=1}^p \hat\varphi_{1i}\langle \bx^{(0)}_j, \by\rangle = \sum_{i=1}^p \hat\varphi_{1i}^2\non \end{equation}\]

so we have \(\hat\theta_1 =1\).

(c) We have

\[\begin{equation} \hat\by^{(1)} = \hat\by^{(0)} + \bb{z}_1 = \hat\by^{(0)} + \sum_{i=1}^p\hat\varphi_{1i}\bx_{i}^{(0)}.\non \end{equation}\]

(d) For each \(j=1,...,p\),

\[\begin{eqnarray} \bx_j^{(1)} &=& \bx_j^{(0)} - \frac{\langle \bb{z}_1, \bx_{j}^{(0)} \rangle}{\langle \bb{z}_1, \bb{z}_1 \rangle}\cdot \bb{z}_1\non\\ &=&\bx_j^{(0)} - \frac{\hat\varphi_{1j}}{\sum_{i=1}^p\varphi_{1i}^2}\bb{z}_1\non\\ &=&\bx_j^{(0)} - \left(\frac{\hat\varphi_{1j}}{\sum_{i=1}^p\hat\varphi_{1i}^2}\right)\sum_{i=1}^p\hat\varphi_{1i}\bx^{(0)}_i.\non \end{eqnarray}\]

Now we move to \(m=2\).

It's easy to see that for any \(j=1,...,p\) we have

\[\begin{eqnarray} \hat\varphi_{2j} &=& \langle \bx^{(1)}_j, \by \rangle \non\\ &=&\langle \bx_j^{(0)}, \by \rangle - \left(\frac{\hat\varphi_{1j}}{\sum_{i=1}^p\hat\varphi_{1i}^2}\right)\sum_{i=1}^p\hat\varphi^2_{1i}\non\\ &=&\hat\varphi_{1j} - \hat\varphi_{1j}=0.\non \end{eqnarray}\]

Therefore we see the algorithm stops in Step 2.