Documentation

Load a multivariate signal by typing at the MATLAB^® prompt:

load ex4mwden  
whos

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

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

load ex4mwden

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.

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

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