Difference between revisions of "User talk:Joyce Whang"
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'''Motivation''' <br> | '''Motivation''' <br> | ||
| − | - PCA is introduced to deal with the problem of excessive dimensionality. | + | - 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. | + | - We can reduce a complex data set to a lower dimension by PCA. <br> |
- | - | ||
Revision as of 03:35, 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.
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