Principal Component Analysis

Principal component analysis has several disadvantages including

• Translation variant
• Scale variant
• Background variant
• Lighting variant

The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Although PCA is used as a tool in exploratory data analysis and making predictive models, its strength is also considered its weakness. This is because of its non-parametric analysis. Since there are no parameters to tweak, the answer of PCA is unique and independent of the users experience. Thus, the answer may not be optimal in all cases.

The basic idea of PCA is given a set of variables, find a set of variables with less redundancy, this gives a good representation for regression analysis. Since redundancy is computed via correlation, it is required for reducing the scale space between vector elements

In Matlab PCA is done via the following function:

[COEFF, SCORE] = princomp(X);

EXAMPLES TO FOLLOW:
TO DO