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# Hilbert Space Theory

Complex random variables form a Hilbert space with inner product

$\langle X, Y \rangle = \mathbb{E}\left[XY^*\right]$

. If we have a random complex vector, then we can use Hilbert Theory in a more efficient manner by looking at the matrix of inner products. For simplicity, we will call this the “inner product” of two complex vectors.The ij-th entry of the matrix is simply the scalar inner product

$\mathbb{E}\left[X_iY_j^*\right]$

where $X_i$

and $Y_j$

are the ith and jth entries of $\boldsymbol{X}$

and $\boldsymbol{Y}$

respectively. This means the matrix is equivalent to the cross correlation $R_{XY}$

between the two vectors. We can also specify the auto-correlation $R_X = \langle \boldsymbol{X}, \boldsymbol{X} \rangle$

and auto-covariance $\Sigma_X = \langle \boldsymbol{X} - \mathbb{E}\left[\boldsymbol{X}\right] , \boldsymbol{X} - \mathbb{E}\left[\boldsymbol{X}\right] \rangle$

. One reason why we can think of this matrix as the inner product is because it also satisfies the properties of inner products. In particular, it is- 1.Linear:$\langle \alpha_1\boldsymbol{V_1}+\alpha_2\boldsymbol{V_2}, \boldsymbol{u} \rangle = \alpha_1\langle \boldsymbol{V_1}, \boldsymbol{u} \rangle + \alpha_2\langle \boldsymbol{V_2}, \boldsymbol{u} \rangle$.
- 2.Reflexive:$\langle \boldsymbol{U}, \boldsymbol{V} \rangle = \langle \boldsymbol{V}, \boldsymbol{U} \rangle ^*$.
- 3.Non-degeneracy:$\langle \boldsymbol{V}, \boldsymbol{V} \rangle = \boldsymbol{0} \Leftrightarrow \boldsymbol{V} = \boldsymbol{0}$.

Since we are thinking of the matrix as an inner product, we can also think of the norm as a matrix.

When thinking of inner products as matrices instead of scalars, we must rewrite the Hilbert Projection Theorem to use matrices instead.

The minimization problem

$\min_{\hat{\boldsymbol{X}}(\boldsymbol{Y})}\|\hat{\boldsymbol{X}}(\boldsymbol{Y}) - \boldsymbol{X}\|^2$

has a unique solution which is a linear function of $\boldsymbol{Y}$

. The error is orthogonal to the linear subspace of $\boldsymbol{Y}$

(i.e $\langle \boldsymbol{X} - \hat{\boldsymbol{X}}, \boldsymbol{Y} \rangle = \boldsymbol{0}$

)When we do a minimization over a matrix, we are minimizing it in a PSD sense, so for any other linear function

$\boldsymbol{X}'$

,

$\|\boldsymbol{X}-\hat{\boldsymbol{X}}\|^2 \preceq \|\boldsymbol{X} - \boldsymbol{X}'\|^2.$

Suppose we have jointly distributed random variables

$Y_0, Y_1,\cdots,Y_n$

. Ideally, we would be able to “de-correlate” them so each new vector $E_0$

captures the new information which is orthogonal to previous random vectors in the sequence. Since vectors of a Hilbert Space operate like vectors in $\mathbb{R}^n$

, we can simply do Gram-Schmidt on the $\{Y_i\}_{i=0}^n$

.Innovations have two key properties.

- 1.$\forall i\neq j,\ \langle E_i, E_j \rangle =0$
- 2.$\forall i,\ \text{span}\{Y_j\}_{j=0}^i = \text{span}\{E_j\}_{j=0}^i$

We can also write innovations in terms of a matrix where

$\boldsymbol{\varepsilon} = A\boldsymbol{Y}$

where $\boldsymbol{\varepsilon} = \begin{bmatrix}E_0 & E_1 & \cdots & E_n\end{bmatrix}^T$

and $\boldsymbol{Y} = \begin{bmatrix}Y_0 & Y_1 & \cdots & Y_n\end{bmatrix}^T$

. Since each $E_i$

only depends on the previous $Y_i$

, then A must be lower triangular, and because we need each $E_i$

to be mutually orthogonal, $R_{\varepsilon}$

should be diagonal. $R_{\varepsilon} = AR_YA^*$

, so if $R_Y \succ 0$

, then we can use its unique LDL decomposition $R_Y = LDL^*$

and let $A = L^{-1}$

.Last modified 1yr ago