Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

This section demonstrates the features of multiscale principal
components analysis provided in the Wavelet
Toolbox™ software.
The toolbox includes the `wmspca`

function
and a **Wavelet Analyzer** app.
This section describes the command-line and app methods, and information
about transferring signal and parameter information between the disk
and the app.

The aim of multiscale PCA is to reconstruct, starting from a multivariate signal and using a simple representation at each resolution level, a simplified multivariate signal. The multiscale principal components generalizes the normal PCA of a multivariate signal represented as a matrix by performing a PCA on the matrices of details of different levels simultaneously. A PCA is also performed on the coarser approximation coefficients matrix in the wavelet domain as well as on the final reconstructed matrix. By selecting the numbers of retained principal components, interesting simplified signals can be reconstructed.

Since you can perform multiscale PCA either from the command line or using the app, this section has subsections covering each method.

This example uses noisy test signals. In this section, you will:

Load a multivariate signal.

Perform a simple multiscale PCA.

Display the original and simplified signals.

Improve the obtained result by retaining less principal components.

Load a multivariate signal by typing at the MATLAB

^{®}prompt:load ex4mwden whos

Name Size Bytes Class `covar`

`4x4`

`128`

`double array`

`x`

`1024x4`

`32768`

`double array`

`x_orig`

`1024x4`

`32768`

`double array`

The data stored in matrix

`x`

comes from two test signals, Blocks and HeavySine, and from their sum and difference, to which multivariate Gaussian white noise has been added.Perform a simple multiscale PCA.

The multiscale PCA combines noncentered PCA on approximations and details in the wavelet domain and a final PCA. At each level, the most significant principal components are selected.

First, set the wavelet parameters:

level= 5; wname = 'sym4';

Then, automatically select the number of retained principal components using Kaiser's rule by typing

npc = 'kais';

Finally, perform multiscale PCA:

[x_sim, qual, npc] = wmspca(x ,level, wname, npc);

Display the original and simplified signals:

kp = 0; for i = 1:4 subplot(4,2,kp+1), plot(x (:,i)); set(gca,'xtick',[]); axis tight; title(['Original signal ',num2str(i)]) subplot(4,2,kp+2), plot(x_sim(:,i)); set(gca,'xtick',[]); axis tight; title(['Simplified signal ',num2str(i)]) kp = kp + 2; end

The results from a compression perspective are good. The percentages reflecting the quality of column reconstructions given by the relative mean square errors are close to 100%.

qual qual = 98.0545 93.2807 97.1172 98.8603

Improve the first result by retaining fewer principal components.

The results can be improved by suppressing noise, because the details at levels 1 to 3 are composed essentially of noise with small contributions from the signal. Removing the noise leads to a crude, but large, denoising effect.

The output argument npc contains the numbers of retained principal components selected by Kaiser's rule:

npc npc = 1 1 1 1 1 2 2

For

`d`

from 1 to 5,`npc(d)`

is the number of retained noncentered principal components (PCs) for details at level d. The number of retained noncentered PCs for approximations at level 5 is`npc(6)`

, and`npc(7)`

is the number of retained PCs for final PCA after wavelet reconstruction. As expected, the rule keeps two principal components, both for the PCA approximations and the final PCA, but one principal component is kept for details at each level.To suppress the details at levels 1 to 3, update the

`npc`

argument as follows:npc(1:3) = zeros(1,3); npc npc = 0 0 0 1 1 2 2

Then, perform multiscale PCA again:

[x_sim, qual, npc] = wmspca(x, level, wname, npc);

Display the original and final simplified signals:

kp = 0; for i = 1:4 subplot(4,2,kp+1), plot(x (:,i)); set(gca,'xtick',[]); axis tight; title(['Original signal ',num2str(i)]); set(gca,'xtick',[]); axis tight; subplot(4,2,kp+2), plot(x_sim(:,i)); set(gca,'xtick',[]); axis tight; title(['Simplified signal ',num2str(i)]) kp = kp + 2; end

As shown, the results are improved.

This section explores multiscale PCA using the Wavelet Analyzer app.

Open the Wavelet Analyzer app by typing

`waveletAnalyzer`

at the command line.Click

**Multiscale Princ. Comp. Analysis**to open the Multiscale Principal Components Analysis tool in the app.Load data.

At the MATLAB command prompt, type

In theload ex4mwden

**Multiscale Princ. Comp. Analysis**tool, select**File**>**Import from Workspace**. When the**Import from Workspace**dialog box appears, select the`x`

variable. Click**OK**to import the multivariate signal. The signal is a matrix containing four columns, where each column is a signal to be simplified.These signals are noisy versions from simple combinations of the two original signals, Blocks and HeavySine and their sum and difference, each with added multivariate Gaussian white noise.

Perform a wavelet decomposition and diagonalize each coefficients matrix.

Use the default values for the

**Wavelet**, the**DWT Extension Mode**, and the decomposition**Level**, and then click**Decompose and Diagonalize**. The tool displays the wavelet approximation and detail coefficients of the decomposition of each signal in the original basis.To get more information about the new bases allowed for performing a PCA for each scale, click

**More on Adapted Basis**. A new figure displays the corresponding eigenvectors and eigenvalues for the matrix of the detail coefficients at level 1.You can change the level or select the coarser approximations or the reconstructed matrix to investigate the different bases. When you finish, click

**Close**.Perform a simple multiscale PCA.

The initial values for PCA lead to retaining all the components. Select

`Kaiser`

from the**Provide default using**drop-down list, and click**Apply**.The results are good from a compression perspective.

Improve the obtained result by retaining fewer principal components.

The results can be improved by suppressing the noise, because the details at levels 1 to 3 are composed essentially of noise with small contributions from the signal, as you can see by careful inspection of the detail coefficients. Removing the noise leads to a crude, but large, denoising effect.

For

**D1**,**D2**and**D3**, select`0`

as the**Nb. of non-centered PC**and click**Apply**.The results are better than those previously obtained. The first signal, which is irregular, is still correctly recovered, while the second signal, which is more regular, is denoised better after this second stage of PCA. You can get more information by clicking

**Residuals**.

The Multiscale Principal Components Analysis tool lets you save the simplified signals to disk. The toolbox creates a MAT-file in the current folder with a name of your choice.

To save the simplified signals from the previous section:

Select

**File > Save Simplified Signals**.Select

**Save Simplified Signals and Parameters**. A dialog box appears that lets you specify a folder and file name for storing the signal.Type the name

`s_ex4mwden`

and click**OK**to save the data.Load the variables into your workspace:

load s_ex4mwden whos

Name Size Bytes Class `PCA_Params`

`1x7`

`2628`

`struct array`

`x`

`1024x4`

`32768`

`double array`

The simplified signals are in matrix

`x`

. The parameters of multiscale PCA are available in`PCA_Params`

:PCA_Params PCA_Params = 1x7 struct array with fields: pc variances npc

`PCA_Params`

is a structure array of length `d+2`

(here,
the maximum decomposition level `d=5`

) such that `PCA_Params(d).pc`

is
the matrix of principal components. The columns are stored in descending
order of the variances. `PCA_Params(d).variances`

is
the principal component variances vector, and `PCA_Params(d).npc`

is
the vector of selected numbers of retained principal components.