Difference between revisions of "User talk:Joyce Whang"

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- PCA is introduced to deal with the problem of excessive dimensionality. <br>
 
- PCA is introduced to deal with the problem of excessive dimensionality. <br>
 
- We can reduce a complex data set to a lower dimension by PCA. <br>
 
- We can reduce a complex data set to a lower dimension by PCA. <br>
- 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. <br>
+
- PCA is defined as the orthogonal projection of the data onto a lower dimensional space such that the variance of the projected data is maximized. <br>

Revision as of 03:41, 9 April 2012

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 that the variance of the projected data is maximized.