# 7. Singular Value Decomposition (SVD)#

## 7.1. Overview#

The **singular value decomposition** (SVD) is a work-horse in applications of least squares projection that
form foundations for many statistical and machine learning methods.

After defining the SVD, we’ll describe how it connects to

**four fundamental spaces**of linear algebraunder-determined and over-determined

**least squares regressions****principal components analysis**(PCA)

Like principal components analysis (PCA), DMD can be thought of as a data-reduction procedure that represents salient patterns by projecting data onto a limited set of factors.

In a sequel to this lecture about Dynamic Mode Decompositions, we’ll describe how SVD’s provide ways rapidly to compute reduced-order approximations to first-order Vector Autoregressions (VARs).

## 7.2. The Setting#

Let \(X\) be an \(m \times n\) matrix of rank \(p\).

Necessarily, \(p \leq \min(m,n)\).

In much of this lecture, we’ll think of \(X\) as a matrix of **data** in which

each column is an

**individual**– a time period or person, depending on the applicationeach row is a

**random variable**describing an attribute of a time period or a person, depending on the application

We’ll be interested in two situations

A

**short and fat**case in which \(m << n\), so that there are many more columns (individuals) than rows (attributes).A

**tall and skinny**case in which \(m >> n\), so that there are many more rows (attributes) than columns (individuals).

We’ll apply a **singular value decomposition** of \(X\) in both situations.

In the \( m < < n\) case in which there are many more individuals \(n\) than attributes \(m\), we can calculate sample moments of a joint distribution by taking averages across observations of functions of the observations.

In this \( m < < n\) case, we’ll look for **patterns** by using a **singular value decomposition** to do a **principal components analysis** (PCA).

In the \(m > > n\) case in which there are many more attributes \(m\) than individuals \(n\) and when we are in a time-series setting in which \(n\) equals the number of time periods covered in the data set \(X\), we’ll proceed in a different way.

We’ll again use a **singular value decomposition**, but now to construct a **dynamic mode decomposition** (DMD)

## 7.3. Singular Value Decomposition#

A **singular value decomposition** of an \(m \times n\) matrix \(X\) of rank \(p \leq \min(m,n)\) is

where

and

\(U\) is an \(m \times m\) orthogonal matrix of

**left singular vectors**of \(X\)Columns of \(U\) are eigenvectors of \(X^\top X\)

\(V\) is an \(n \times n\) orthogonal matrix of

**right singular values**of \(X\)Columns of \(V\) are eigenvectors of \(X X^\top \)

\(\Sigma\) is an \(m \times n\) matrix in which the first \(p\) places on its main diagonal are positive numbers \(\sigma_1, \sigma_2, \ldots, \sigma_p\) called

**singular values**; remaining entries of \(\Sigma\) are all zeroThe \(p\) singular values are positive square roots of the eigenvalues of the \(m \times m\) matrix \(X X^\top \) and also of the \(n \times n\) matrix \(X^\top X\)

We adopt a convention that when \(U\) is a complex valued matrix, \(U^\top \) denotes the

**conjugate-transpose**or**Hermitian-transpose**of \(U\), meaning that \(U_{ij}^\top \) is the complex conjugate of \(U_{ji}\).Similarly, when \(V\) is a complex valued matrix, \(V^\top \) denotes the

**conjugate-transpose**or**Hermitian-transpose**of \(V\)

The matrices \(U,\Sigma,V\) entail linear transformations that reshape in vectors in the following ways:

multiplying vectors by the unitary matrices \(U\) and \(V\)

**rotates**them, but leaves**angles between vectors**and**lengths of vectors**unchanged.multiplying vectors by the diagonal matrix \(\Sigma\) leaves

**angles between vectors**unchanged but**rescales**vectors.

Thus, representation (7.1) asserts that multiplying an \(n \times 1\) vector \(y\) by the \(m \times n\) matrix \(X\) amounts to performing the following three multiplcations of \(y\) sequentially:

**rotating**\(y\) by computing \(V^\top y\)**rescaling**\(V^\top y\) by multipying it by \(\Sigma\)**rotating**\(\Sigma V^\top y\) by multiplying it by \(U\)

This structure of the \(m \times n\) matrix \(X\) opens the door to constructing systems
of data **encoders** and **decoders**.

Thus,

\(V^\top y\) is an encoder

\(\Sigma\) is an operator to be applied to the encoded data

\(U\) is a decoder to be applied to the output from applying operator \(\Sigma\) to the encoded data

We’ll apply this circle of ideas later in this lecture when we study Dynamic Mode Decomposition.

**Road Ahead**

What we have described above is called a **full** SVD.

In a **full** SVD, the shapes of \(U\), \(\Sigma\), and \(V\) are \(\left(m, m\right)\), \(\left(m, n\right)\), \(\left(n, n\right)\), respectively.

Later we’ll also describe an **economy** or **reduced** SVD.

Before we study a **reduced** SVD we’ll say a little more about properties of a **full** SVD.

## 7.4. Four Fundamental Subspaces#

Let \({\mathcal C}\) denote a column space, \({\mathcal N}\) denote a null space, and \({\mathcal R}\) denote a row space.

Let’s start by recalling the four fundamental subspaces of an \(m \times n\) matrix \(X\) of rank \(p\).

The

**column space**of \(X\), denoted \({\mathcal C}(X)\), is the span of the columns of \(X\), i.e., all vectors \(y\) that can be written as linear combinations of columns of \(X\). Its dimension is \(p\).The

**null space**of \(X\), denoted \({\mathcal N}(X)\) consists of all vectors \(y\) that satisfy \(X y = 0\). Its dimension is \(m-p\).The

**row space**of \(X\), denoted \({\mathcal R}(X)\) is the column space of \(X^\top \). It consists of all vectors \(z\) that can be written as linear combinations of rows of \(X\). Its dimension is \(p\).The

**left null space**of \(X\), denoted \({\mathcal N}(X^\top )\), consist of all vectors \(z\) such that \(X^\top z =0\). Its dimension is \(n-p\).

For a full SVD of a matrix \(X\), the matrix \(U\) of left singular vectors and the matrix \(V\) of right singular vectors contain orthogonal bases for all four subspaces.

They form two pairs of orthogonal subspaces that we’ll describe now.

Let \(u_i, i = 1, \ldots, m\) be the \(m\) column vectors of \(U\) and let \(v_i, i = 1, \ldots, n\) be the \(n\) column vectors of \(V\).

Let’s write the full SVD of X as

where \( \Sigma_p\) is a \(p \times p\) diagonal matrix with the \(p\) singular values on the diagonal and

Representation (7.2) implies that

or

or

Equations (7.4) tell how the transformation \(X\) maps a pair of orthonormal vectors \(v_i, v_j\) for \(i\) and \(j\) both less than or equal to the rank \(p\) of \(X\) into a pair of orthonormal vectors \(u_i, u_j\).

Equations (7.3) assert that

Taking transposes on both sides of representation (7.2) implies

or

or

Notice how equations (7.6) assert that the transformation \(X^\top \) maps a pairsof distinct orthonormal vectors \(u_i, u_j\) for \(i\) and \(j\) both less than or equal to the rank \(p\) of \(X\) into a pair of distinct orthonormal vectors \(v_i, v_j\) .

Equations (7.5) assert that

Thus, taken together, the systems of quations (7.3) and (7.5) describe the four fundamental subspaces of \(X\) in the following ways:

Since \(U\) and \(V\) are both orthonormal matrices, collection (7.7) asserts that

\(U_L\) is an orthonormal basis for the column space of \(X\)

\(U_R\) is an orthonormal basis for the null space of \(X^\top \)

\(V_L\) is an orthonormal basis for the row space of \(X\)

\(V_R\) is an orthonormal basis for the null space of \(X\)

We have verified the four claims in (7.7) simply by performing the multiplications called for by the right side of (7.2) and reading them.

The claims in (7.7) and the fact that \(U\) and \(V\) are both unitary (i.e, orthonormal) matrices imply that

the column space of \(X\) is orthogonal to the null space of of \(X^\top \)

the null space of \(X\) is orthogonal to the row space of \(X\)

Sometimes these properties are described with the following two pairs of orthogonal complement subspaces:

\({\mathcal C}(X)\) is the orthogonal complement of \( {\mathcal N}(X^\top )\)

\({\mathcal R}(X)\) is the orthogonal complement \({\mathcal N}(X)\)

Let’s do an example.

```
import numpy as np
import numpy.linalg as LA
import matplotlib.pyplot as plt
%matplotlib inline
```

Having imported these modules, let’s do the example.

```
np.set_printoptions(precision=2)
# Define the matrix
A = np.array([[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8],
[5, 6, 7, 8, 9]])
# Compute the SVD of the matrix
U, S, V = np.linalg.svd(A,full_matrices=True)
# Compute the rank of the matrix
rank = np.linalg.matrix_rank(A)
# Print the rank of the matrix
print("Rank of matrix:\n", rank)
print("S: \n", S)
# Compute the four fundamental subspaces
row_space = U[:, :rank]
col_space = V[:, :rank]
null_space = V[:, rank:]
left_null_space = U[:, rank:]
print("U:\n", U)
print("Column space:\n", col_space)
print("Left null space:\n", left_null_space)
print("V.T:\n", V.T)
print("Row space:\n", row_space.T)
print("Right null space:\n", null_space.T)
```

```
Rank of matrix:
2
S:
[2.69e+01 1.86e+00 1.20e-15 2.24e-16 5.82e-17]
U:
[[-0.27 -0.73 0.63 -0.06 0.06]
[-0.35 -0.42 -0.69 -0.45 0.12]
[-0.43 -0.11 -0.24 0.85 0.12]
[-0.51 0.19 0.06 -0.1 -0.83]
[-0.59 0.5 0.25 -0.24 0.53]]
Column space:
[[-0.27 -0.35]
[ 0.73 0.42]
[ 0.32 -0.65]
[ 0.54 -0.39]
[-0.06 -0.35]]
Left null space:
[[ 0.63 -0.06 0.06]
[-0.69 -0.45 0.12]
[-0.24 0.85 0.12]
[ 0.06 -0.1 -0.83]
[ 0.25 -0.24 0.53]]
V.T:
[[-0.27 0.73 0.32 0.54 -0.06]
[-0.35 0.42 -0.65 -0.39 -0.35]
[-0.43 0.11 0.02 -0.29 0.85]
[-0.51 -0.19 0.61 -0.41 -0.4 ]
[-0.59 -0.5 -0.31 0.55 -0.04]]
Row space:
[[-0.27 -0.35 -0.43 -0.51 -0.59]
[-0.73 -0.42 -0.11 0.19 0.5 ]]
Right null space:
[[-0.43 0.11 0.02 -0.29 0.85]
[-0.51 -0.19 0.61 -0.41 -0.4 ]
[-0.59 -0.5 -0.31 0.55 -0.04]]
```

## 7.5. Eckart-Young Theorem#

Suppose that we want to construct the best rank \(r\) approximation of an \(m \times n\) matrix \(X\).

By best we mean a matrix \(X_r\) of rank \(r < p\) that, among all rank \(r\) matrices, minimizes

where \( || \cdot || \) denotes a norm of a matrix \(X\) and where \(X_r\) belongs to the space of all rank \(r\) matrices of dimension \(m \times n\).

Three popular **matrix norms** of an \(m \times n\) matrix \(X\) can be expressed in terms of the singular values of \(X\)

the

**spectral**or \(l^2\) norm \(|| X ||_2 = \max_{y \in \textbf{R}^n} \frac{||X y ||}{||y||} = \sigma_1\)the

**Frobenius**norm \(||X ||_F = \sqrt{\sigma_1^2 + \cdots + \sigma_p^2}\)the

**nuclear**norm \( || X ||_N = \sigma_1 + \cdots + \sigma_p \)

The Eckart-Young theorem states that for each of these three norms, same rank \(r\) matrix is best and that it equals

You can read about the Eckart-Young theorem and some of its uses here https://en.wikipedia.org/wiki/Low-rank_approximation.

We’ll make use of this theorem when we discuss principal components analysis (PCA) and also dynamic mode decomposition (DMD).

## 7.6. Full and Reduced SVD’s#

Up to now we have described properties of a **full** SVD in which shapes of \(U\), \(\Sigma\), and \(V\) are \(\left(m, m\right)\), \(\left(m, n\right)\), \(\left(n, n\right)\), respectively.

There is an alternative bookkeeping convention called an **economy** or **reduced** SVD in which the shapes of \(U, \Sigma\) and \(V\) are different from what they are in a full SVD.

Thus, note that because we assume that \(X\) has rank \(p\), there are only \(p\) nonzero singular values, where \(p=\textrm{rank}(X)\leq\min\left(m, n\right)\).

A **reduced** SVD uses this fact to express \(U\), \(\Sigma\), and \(V\) as matrices with shapes \(\left(m, p\right)\), \(\left(p, p\right)\), \(\left( n, p\right)\).

You can read about reduced and full SVD here https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html

For a full SVD,

But not all these properties hold for a **reduced** SVD.

Which properties hold depend on whether we are in a **tall-skinny** case or a **short-fat** case.

In a

**tall-skinny**case in which \(m > > n\), for a**reduced**SVD

In a

**short-fat**case in which \(m < < n\), for a**reduced**SVD

When we study Dynamic Mode Decomposition below, we shall want to remember these properties when we use a reduced SVD to compute some DMD representations.

Let’s do an exercise to compare **full** and **reduced** SVD’s.

To review,

in a

**full**SVD\(U\) is \(m \times m\)

\(\Sigma\) is \(m \times n\)

\(V\) is \(n \times n\)

in a

**reduced**SVD\(U\) is \(m \times p\)

\(\Sigma\) is \(p\times p\)

\(V\) is \(n \times p\)

First, let’s study a case in which \(m = 5 > n = 2\).

(This is a small example of the **tall-skinny** case that will concern us when we study **Dynamic Mode Decompositions** below.)

```
import numpy as np
X = np.random.rand(5,2)
U, S, V = np.linalg.svd(X,full_matrices=True) # full SVD
Uhat, Shat, Vhat = np.linalg.svd(X,full_matrices=False) # economy SVD
print('U, S, V =')
U, S, V
```

```
U, S, V =
```

```
(array([[-0.7 , -0.25, -0.24, -0.38, -0.49],
[-0.36, -0.36, -0.46, 0.41, 0.6 ],
[-0.32, -0.37, 0.85, 0.11, 0.16],
[-0.32, 0.47, 0.06, 0.73, -0.37],
[-0.41, 0.67, 0.1 , -0.37, 0.49]]),
array([1.57, 0.67]),
array([[-0.65, -0.76],
[ 0.76, -0.65]]))
```

```
print('Uhat, Shat, Vhat = ')
Uhat, Shat, Vhat
```

```
Uhat, Shat, Vhat =
```

```
(array([[-0.7 , -0.25],
[-0.36, -0.36],
[-0.32, -0.37],
[-0.32, 0.47],
[-0.41, 0.67]]),
array([1.57, 0.67]),
array([[-0.65, -0.76],
[ 0.76, -0.65]]))
```

```
rr = np.linalg.matrix_rank(X)
print(f'rank of X = {rr}')
```

```
rank of X = 2
```

**Properties:**

Where \(U\) is constructed via a full SVD, \(U^\top U = I_{p\times p}\) and \(U U^\top = I_{m \times m}\)

Where \(\hat U\) is constructed via a reduced SVD, although \(\hat U^\top \hat U = I_{p\times p}\), it happens that \(\hat U \hat U^\top \neq I_{m \times m}\)

We illustrate these properties for our example with the following code cells.

```
UTU = U.T@U
UUT = U@U.T
print('UUT, UTU = ')
UUT, UTU
```

```
UUT, UTU =
```

```
(array([[ 1.00e+00, 8.88e-18, -7.16e-17, 1.20e-16, 1.17e-16],
[ 8.88e-18, 1.00e+00, -1.23e-18, -1.58e-17, -1.40e-16],
[-7.16e-17, -1.23e-18, 1.00e+00, -3.47e-17, -7.84e-17],
[ 1.20e-16, -1.58e-17, -3.47e-17, 1.00e+00, 1.86e-17],
[ 1.17e-16, -1.40e-16, -7.84e-17, 1.86e-17, 1.00e+00]]),
array([[ 1.00e+00, -1.19e-16, 4.08e-17, -2.06e-18, 9.02e-17],
[-1.19e-16, 1.00e+00, 2.11e-17, -1.11e-16, -7.91e-17],
[ 4.08e-17, 2.11e-17, 1.00e+00, -3.27e-17, -3.65e-17],
[-2.06e-18, -1.11e-16, -3.27e-17, 1.00e+00, -6.56e-17],
[ 9.02e-17, -7.91e-17, -3.65e-17, -6.56e-17, 1.00e+00]]))
```

```
UhatUhatT = Uhat@Uhat.T
UhatTUhat = Uhat.T@Uhat
print('UhatUhatT, UhatTUhat= ')
UhatUhatT, UhatTUhat
```

```
UhatUhatT, UhatTUhat=
```

```
(array([[ 0.56, 0.34, 0.32, 0.11, 0.12],
[ 0.34, 0.26, 0.25, -0.05, -0.1 ],
[ 0.32, 0.25, 0.24, -0.07, -0.12],
[ 0.11, -0.05, -0.07, 0.32, 0.45],
[ 0.12, -0.1 , -0.12, 0.45, 0.62]]),
array([[ 1.00e+00, -1.19e-16],
[-1.19e-16, 1.00e+00]]))
```

**Remarks:**

The cells above illustrate application of the `fullmatrices=True`

and `full-matrices=False`

options.
Using `full-matrices=False`

returns a reduced singular value decomposition.

The **full** and **reduced** SVd’s both accurately decompose an \(m \times n\) matrix \(X\)

When we study Dynamic Mode Decompositions below, it will be important for us to remember the preceding properties of full and reduced SVD’s in such tall-skinny cases.

Now let’s turn to a short-fat case.

To illustrate this case, we’ll set \(m = 2 < 5 = n \) and compute both full and reduced SVD’s.

```
import numpy as np
X = np.random.rand(2,5)
U, S, V = np.linalg.svd(X,full_matrices=True) # full SVD
Uhat, Shat, Vhat = np.linalg.svd(X,full_matrices=False) # economy SVD
print('U, S, V = ')
U, S, V
```

```
U, S, V =
```

```
(array([[ 0.68, -0.73],
[ 0.73, 0.68]]),
array([1.47, 0.52]),
array([[ 0.35, 0.09, 0.51, 0.7 , 0.35],
[ 0. , 0.05, 0.45, -0.64, 0.62],
[-0.34, -0.59, 0.61, 0. , -0.4 ],
[-0.86, 0.39, 0.03, 0.25, 0.21],
[-0.14, -0.7 , -0.4 , 0.18, 0.55]]))
```

```
print('Uhat, Shat, Vhat = ')
Uhat, Shat, Vhat
```

```
Uhat, Shat, Vhat =
```

```
(array([[ 0.68, -0.73],
[ 0.73, 0.68]]),
array([1.47, 0.52]),
array([[ 0.35, 0.09, 0.51, 0.7 , 0.35],
[ 0. , 0.05, 0.45, -0.64, 0.62]]))
```

Let’s verify that our reduced SVD accurately represents \(X\)

```
SShat=np.diag(Shat)
np.allclose(X, Uhat@SShat@Vhat)
```

```
True
```

## 7.7. Polar Decomposition#

A **reduced** singular value decomposition (SVD) of \(X\) is related to a **polar decomposition** of \(X\)

where

Here

\(S\) is an \(m \times m\)

**symmetric**matrix\(Q\) is an \(m \times n\)

**orthogonal**matrix

and in our reduced SVD

\(U\) is an \(m \times p\) orthonormal matrix

\(\Sigma\) is a \(p \times p\) diagonal matrix

\(V\) is an \(n \times p\) orthonormal

## 7.8. Application: Principal Components Analysis (PCA)#

Let’s begin with a case in which \(n >> m\), so that we have many more individuals \(n\) than attributes \(m\).

The matrix \(X\) is **short and fat** in an \(n >> m\) case as opposed to a **tall and skinny** case with \(m > > n \) to be discussed later.

We regard \(X\) as an \(m \times n\) matrix of **data**:

where for \(j = 1, \ldots, n\) the column vector \(X_j = \begin{bmatrix}X_{1j}\\X_{2j}\\\vdots\\X_{mj}\end{bmatrix}\) is a vector of observations on variables \(\begin{bmatrix}x_1\\x_2\\\vdots\\x_m\end{bmatrix}\).

In a **time series** setting, we would think of columns \(j\) as indexing different **times** at which random variables are observed, while rows index different random variables.

In a **cross section** setting, we would think of columns \(j\) as indexing different **individuals** for which random variables are observed, while rows index different **attributes**.

The number of positive singular values equals the rank of matrix \(X\).

Arrange the singular values in decreasing order.

Arrange the positive singular values on the main diagonal of the matrix \(\Sigma\) of into a vector \(\sigma_R\).

Set all other entries of \(\Sigma\) to zero.

## 7.9. Relationship of PCA to SVD#

To relate a SVD to a PCA (principal component analysis) of data set \(X\), first construct the SVD of the data matrix \(X\):

where

In equation (7.9), each of the \(m \times n\) matrices \(U_{j}V_{j}^\top \) is evidently of rank \(1\).

Thus, we have

Here is how we would interpret the objects in the matrix equation (7.10) in a time series context:

\( \textrm{for each} \ k=1, \ldots, n \), the object \(\lbrace V_{kj} \rbrace_{j=1}^n\) is a time series for the \(k\)th

**principal component**\(U_j = \begin{bmatrix}U_{1k}\\U_{2k}\\\ldots\\U_{mk}\end{bmatrix} \ k=1, \ldots, m\) is a vector of

**loadings**of variables \(X_i\) on the \(k\)th principal component, \(i=1, \ldots, m\)\(\sigma_k \) for each \(k=1, \ldots, p\) is the strength of \(k\)th

**principal component**, where strength means contribution to the overall covariance of \(X\).

## 7.10. PCA with Eigenvalues and Eigenvectors#

We now use an eigen decomposition of a sample covariance matrix to do PCA.

Let \(X_{m \times n}\) be our \(m \times n\) data matrix.

Let’s assume that sample means of all variables are zero.

We can assure this by **pre-processing** the data by subtracting sample means.

Define a sample covariance matrix \(\Omega\) as

Then use an eigen decomposition to represent \(\Omega\) as follows:

Here

\(P\) is \(m×m\) matrix of eigenvectors of \(\Omega\)

\(\Lambda\) is a diagonal matrix of eigenvalues of \(\Omega\)

We can then represent \(X\) as

where

and

We can verify that

It follows that we can represent the data matrix \(X\) as

To reconcile the preceding representation with the PCA that we had obtained earlier through the SVD, we first note that \(\epsilon_j^2=\lambda_j\equiv\sigma^2_j\).

Now define \(\tilde{\epsilon_j} = \frac{\epsilon_j}{\sqrt{\lambda_j}}\), which implies that \(\tilde{\epsilon}_j\tilde{\epsilon}_j^\top =1\).

Therefore

which agrees with

provided that we set

\(U_j=P_j\) (a vector of loadings of variables on principal component \(j\))

\({V_k}^{T}=\tilde{\epsilon_k}\) (the \(k\)th principal component)

Because there are alternative algorithms for computing \(P\) and \(U\) for given a data matrix \(X\), depending on algorithms used, we might have sign differences or different orders of eigenvectors.

We can resolve such ambiguities about \(U\) and \(P\) by

sorting eigenvalues and singular values in descending order

imposing positive diagonals on \(P\) and \(U\) and adjusting signs in \(V^\top \) accordingly

## 7.11. Connections#

To pull things together, it is useful to assemble and compare some formulas presented above.

First, consider an SVD of an \(m \times n\) matrix:

Compute:

Compare representation (7.12) with equation (7.11) above.

Evidently, \(U\) in the SVD is the matrix \(P\) of eigenvectors of \(XX^\top \) and \(\Sigma \Sigma^\top \) is the matrix \(\Lambda\) of eigenvalues.

Second, let’s compute

Thus, the matrix \(V\) in the SVD is the matrix of eigenvectors of \(X^\top X\)

Summarizing and fitting things together, we have the eigen decomposition of the sample covariance matrix

where \(P\) is an orthogonal matrix.

Further, from the SVD of \(X\), we know that

where \(U\) is an orthonal matrix.

Thus, \(P = U\) and we have the representation of \(X\)

It follows that

Note that the preceding implies that

so that everything fits together.

Below we define a class `DecomAnalysis`

that wraps PCA and SVD for a given a data matrix `X`

.

```
class DecomAnalysis:
"""
A class for conducting PCA and SVD.
"""
def __init__(self, X, n_component=None):
self.X = X
self.Ω = (X @ X.T)
self.m, self.n = X.shape
self.r = LA.matrix_rank(X)
if n_component:
self.n_component = n_component
else:
self.n_component = self.m
def pca(self):
𝜆, P = LA.eigh(self.Ω) # columns of P are eigenvectors
ind = sorted(range(𝜆.size), key=lambda x: 𝜆[x], reverse=True)
# sort by eigenvalues
self.𝜆 = 𝜆[ind]
P = P[:, ind]
self.P = P @ diag_sign(P)
self.Λ = np.diag(self.𝜆)
self.explained_ratio_pca = np.cumsum(self.𝜆) / self.𝜆.sum()
# compute the N by T matrix of principal components
self.𝜖 = self.P.T @ self.X
P = self.P[:, :self.n_component]
𝜖 = self.𝜖[:self.n_component, :]
# transform data
self.X_pca = P @ 𝜖
def svd(self):
U, 𝜎, VT = LA.svd(self.X)
ind = sorted(range(𝜎.size), key=lambda x: 𝜎[x], reverse=True)
# sort by eigenvalues
d = min(self.m, self.n)
self.𝜎 = 𝜎[ind]
U = U[:, ind]
D = diag_sign(U)
self.U = U @ D
VT[:d, :] = D @ VT[ind, :]
self.VT = VT
self.Σ = np.zeros((self.m, self.n))
self.Σ[:d, :d] = np.diag(self.𝜎)
𝜎_sq = self.𝜎 ** 2
self.explained_ratio_svd = np.cumsum(𝜎_sq) / 𝜎_sq.sum()
# slicing matrices by the number of components to use
U = self.U[:, :self.n_component]
Σ = self.Σ[:self.n_component, :self.n_component]
VT = self.VT[:self.n_component, :]
# transform data
self.X_svd = U @ Σ @ VT
def fit(self, n_component):
# pca
P = self.P[:, :n_component]
𝜖 = self.𝜖[:n_component, :]
# transform data
self.X_pca = P @ 𝜖
# svd
U = self.U[:, :n_component]
Σ = self.Σ[:n_component, :n_component]
VT = self.VT[:n_component, :]
# transform data
self.X_svd = U @ Σ @ VT
def diag_sign(A):
"Compute the signs of the diagonal of matrix A"
D = np.diag(np.sign(np.diag(A)))
return D
```

We also define a function that prints out information so that we can compare decompositions obtained by different algorithms.

```
def compare_pca_svd(da):
"""
Compare the outcomes of PCA and SVD.
"""
da.pca()
da.svd()
print('Eigenvalues and Singular values\n')
print(f'λ = {da.λ}\n')
print(f'σ^2 = {da.σ**2}\n')
print('\n')
# loading matrices
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
plt.suptitle('loadings')
axs[0].plot(da.P.T)
axs[0].set_title('P')
axs[0].set_xlabel('m')
axs[1].plot(da.U.T)
axs[1].set_title('U')
axs[1].set_xlabel('m')
plt.show()
# principal components
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
plt.suptitle('principal components')
axs[0].plot(da.ε.T)
axs[0].set_title('ε')
axs[0].set_xlabel('n')
axs[1].plot(da.VT[:da.r, :].T * np.sqrt(da.λ))
axs[1].set_title('$V^\top *\sqrt{\lambda}$')
axs[1].set_xlabel('n')
plt.show()
```

For an example PCA applied to analyzing the structure of intelligence tests see this lecture Multivariable Normal Distribution.

Look at parts of that lecture that describe and illustrate the classic factor analysis model.

As mentioned earlier, in a sequel to this lecture about Dynamic Mode Decompositions, we’ll describe how SVD’s provide ways rapidly to compute reduced-order approximations to first-order Vector Autoregressions (VARs).