Difference between revisions of "User talk:Joyce Whang"

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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.
 
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.
  
[Slide 1] Motivation
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'''Motivation'''
- Given high dimensional data, we want to "summarize" the data by  
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- PCA is introduced to deal with the problem of excessive dimensionality.
 
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- We can reduce a complex data set to a lower dimension by PCA.
[Slide 2]
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Revision as of 03:34, 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.

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. -