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Principal Component Analysis (PCA)

Uses:-

1. Noise Filtering
2. Visualization
3. Feature Extraction
4. Stock Market Predictions
5. Gene Data Analysis

Goals:-

• Identify patterns in data
• Detect the correlation between variables
• Reduce the dimensions of a d-dimensional dataset by projecting it onto a (k) - dimensional subspace (where k < d)

Main Functions of the PCA items:-

• Standardize the data
• Obtain the Eigen vectors and Eigen values from the covariance matrix or correlation matrix, or perform Singular Vector Decomposition.
• Sort Eigen values in descending order and choose the k Eigen vectors that correspond to the k largest Eigen values where k is the number of dimensions of the new feature subspace (k <= d).
• Construct the projection matrix W from the selected k Eigen vectors.
• Transform the original dataset X via W to obtain a k-dimensional feature subspace Y

https://plot.ly/ipython-notebooks/principal-component-analysis/

http://setosa.io/ev/principal-component-analysis/

PCA is attempting to:-

• Learn about the relationship between X and Y values
• Find list of principal axes

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Download Resources

• Python Notebook
• Dataset
• Principal Component Analysis (PCA) | R Code

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