CODEWITHSUNDEEP
matlabimage-processingpca
Andres Lopez
Andres Lopez

Reputation: 151

dimension reduction using pca for FastICA

I am trying to develop a system for image classification. I am using following the article:

INDEPENDENT COMPONENT ANALYSIS (ICA) FOR TEXTURE CLASSIFICATION by Dr. Dia Abu Al Nadi and Ayman M. Mansour

In a paragraph it says:

Given the above texture images, the Independent Components are learned by the method outlined above. The (8 x 8) ICA basis function for the above textures are shown in Figure 2. respectively. The dimension is reduced by PCA, resulting in a total of 40 functions. Note that independent components from different windows size are different.

The "method outlined above" is FastICA, the textures are taken from Brodatz album , each texture image has 640x640 pixels. My question is:

What the authors means with "The dimension is reduced by PCA, resulting in a total of 40 functions.", and how can I get that functions using matlab?

Upvotes: 0

Views: 2080

Answers (2)

Simeng Yue
Simeng Yue

Reputation: 11

PCA reduce the dimension of data,ICA extracts the components of the data of which dimension must <= data dimension

Upvotes: 1

DCS
DCS

Reputation: 3384

PCA (Principal Component Analysis) is a method for finding an orthogonal basis (think of a coordinate system) for a high-dimensional (data) space. The "axes" of the PCA basis are sorted by variance, i.e. along the first PCA "axis" your data has the largest variance, along the second "axis" the second largest variance, etc.

This is exploited for dimension reduction: Say you have 1000 dimensional data. Then you do a PCA, transform your data into the PCA basis and throw away all but the first 20 dimensions (just an example). If your data follows a certain statistical distribution, then chances are that the 20 PCA dimensions describe your data almost as well as the 64 original dimensions did. There are methods for finding the number of dimensions to use, but that is beyond scope here.

Computationally, PCA amounts to finding the Eigen-decomposition of your data's covariance matrix, in Matlab: [V,D] = eig(cov(MyData)).

Note that if you want to work with these concepts you should do some serious reading. A classic article on what you can do with PCA on image data is Turk and Pentland's Eigenfaces. It also gives some background in an understandable way.

Upvotes: 2

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