# User talk:Joyce Whang

Revision as of 03:46, 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 in such a way that the variance of the projected data is maximized.

- Intuition: given high-dimensional data, some attributes are redundant. We can compress the data without much loss of information by PCA.