Difference between revisions of "Principal Component Analysis (PCA)"
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* [[AI Solver]] | * [[AI Solver]] | ||
* [[...find outliers]] | * [[...find outliers]] | ||
| + | * [[Clustering]] | ||
* [[Anomaly Detection]] | * [[Anomaly Detection]] | ||
* [[Dimensional Reduction Algorithms]] | * [[Dimensional Reduction Algorithms]] | ||
Revision as of 20:39, 22 April 2019
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Clustering
- 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
