User talk:Joyce Whang
Revision as of 03:39, 9 April 2012 by Joyce Whang (talk | contribs)
In this term project, I will make a lecture about Principal Component Analysis (PCA). PCA is one of the most widely used techniques for linear dimensionality reduction.
1. Motivation
- PCA is introduced to deal with the problem of excessive dimensionality.
- We can reduce a complex data set to a lower dimension by PCA.
- PCA is defined as the orthogonal projection of the data onto a lower dimensional space such a way that the variance of the projected data is maximized.
