Difference between revisions of "Principal Component Analysis (PCA)"
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* [[Dimensional Reduction Algorithms]] | * [[Dimensional Reduction Algorithms]] | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
| + | |||
| + | a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis. | ||
| + | |||
| + | # Center (and standardize) data | ||
| + | # First principal component axis | ||
| + | ## Across centroid of data cloud | ||
| + | ## Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud | ||
| + | # Second principal component axis | ||
| + | ## Orthogonal to first principal component | ||
| + | ## Along maximum variation in the data | ||
| + | # First PCA axis becomes x-axis and second PCA axis y-axis | ||
| + | # Continue process until the necessary number of principal components is obtained | ||
Revision as of 20:03, 22 April 2019
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- Anomaly Detection
- Dimensional Reduction Algorithms
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
a data reduction technique that allows to simplify multidimensional data sets to 2 or 3 dimensions for plotting purposes and visual variance analysis.
- Center (and standardize) data
- First principal component axis
- Across centroid of data cloud
- Distance of each point to that line is minimized, so that it crosses the maximum variation of the data cloud
- Second principal component axis
- Orthogonal to first principal component
- Along maximum variation in the data
- First PCA axis becomes x-axis and second PCA axis y-axis
- Continue process until the necessary number of principal components is obtained
NumXL
