Difference between revisions of "User talk:Joyce Whang"
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- 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 in 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 in such a way that the variance of the projected data is maximized. <br> | ||
| + | - Intuition: given high-dimensional data, some attributes are redundant. We can compress the data without much loss of information by PCA. | ||
Latest revision as of 03:46, 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 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.
