How to do data dimensionailty reduction?

Question

I have a set of 25 images of label 'Infected' and 25 images of label 'Normal'. I am trying to extract the dual-tree complex wavelet transform based coefficients as features for each of the images.

My code to obtain coefficients using DT-CWT ia as follows:

I = imread('infected_img1.jpg'); %read image
I = rgb2gray(I); %rgb ro gray-scale
L = 6; %no. of levels for wavelet decomposition
I = reshape(I',1,size(I,1)*size(I,2)); %change into a vector
I = [I,zeros(1,2^L - rem(length(I),2^L))]; %pad zeros to make dim(I) a multiple of 2^L
I = double(I);
dt = dddtree('cplxdt',I,L,'dtf3'); %perform DT-CWT
dt_Coeffs = (dt.cfs{L}(:,:,1) + 1i*dt.cfs{L}(:,:,2)); %extract coefficents at Level 6

Now, since I have 24 more images to extract coefficients from, I do this block for each of the images. My ultimate aim is to append all coefficient vectors generated in each iteration to form a matrix. But each image tends to give a different sized coefficient vector.

I want to know about some dimension reduction method that can reduce each vector to a uniform size and at the same time preserve its information. Can anyone suggest methods with a good amount of clarity?

Why don't you zero pad to the longest vector length instead? You can't shrink something (i.e. remove information) and still preserve all of the information. — IKavanagh, Sep 22 '15 at 20:15

score 0 · Answer 1 · answered Sep 22 '15 at 20:23

As I mentioned in my comment,

You can't shrink something (i.e. remove information) and still preserve all of the information.

Instead you can pad all of the vectors to the length of the largest vector and then concatenate them to create a matrix. You could program your own method but in the spirit of not reinventing the wheel I've previously used padcat(). It is very easy to use and pads with NaN but you can easily change this to 0.

Here's an example usage which also contains a handy conversion from NaN to 0.

>> a = [1 2 3 4];
>> b = [1 2 3];
>> c = padcat(a, b);
c =

     1     2     3     4
     1     2     3   NaN
>> c(isnan(c)) = 0
c =

     1     2     3     4
     1     2     3     0

thanks for the input @IKavanagh. What about methods like Kernel-PCA, PCA and SVD etc? I am just asking — vishnu, Sep 23 '15 at 15:39
@vishnu I'm not familiar with Kernel-PCA or PCA and no you can't use SVD. — IKavanagh, Sep 23 '15 at 15:50

How to do data dimensionailty reduction?

1 Answers1